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Follow SEJ on Twitter @sejournal

Follow SEJ on Twitter @sejournal

Follow SEJ on Twitter @sejournal

Follow SEJ on Twitter @sejournal

Follow SEJ on Twitter @sejournal

Follow SEJ on Twitter @sejournal

Follow SEJ on Twitter @sejournal

Follow SEJ on Twitter @sejournal

Follow SEJ on Twitter @sejournal

Although emotional burnout of prison staff is costly to all involved, it has not received the kind of research attention that is warranted. This exploratory study focused on the impact of job characteristics on the emotional exhaustion dimension of burnout of prison staff. Using data from 272 staff members at a Midwestern state prison, this study found that both job feedback and job autonomy had negative effects on the index of emotional exhaustion burnout; however, both supervision and job variety had nonsignificant effects. The study further discussed possible reasons for both the significant and nonsignificant relationships.

Much of the literature regarding correctional officer job stress and job satisfaction has examined the roles of organizational and demographic variables. This study examined the relationships among generation, job stress, and job satisfaction of correctional officers in a southern prison system. Hierarchical multiple regression was used to assess the relative impact of demographic variables and generation on job stress and job satisfaction, as well as the impact that stress and satisfaction have on one another. Results indicate that generation membership and job stress significantly shape correctional officers’ perceptions regarding job satisfaction. Conversely, gender and job satisfaction significantly influence job stress. The authors recommend that further research on generational differences among correctional officers be undertaken to assess the overall impact that generation has on employee perceptions of correctional employment.

As the federal courts have established the right of inmates to seek postconviction relief, prisons systems have struggled with a variety of strategies to come into compliance. Using data from a national survey of prisons, this study describes court access strategies employed by state correctional systems and examines how prison contextual characteristics, such as security level, population size, and the court ruling in Lewis v. Casey (1996) affect their use. Results indicate that strategies are influenced by size, security level and demand for legal services, and offer evidence of the adverse effects of the Lewis decision on prison law libraries.

Research on prisoner reentry has largely neglected the perspective of formerly incarcerated persons concerning the stigma and discrimination they face in society. The purpose of this study is to address this gap by examining whether formerly incarcerated persons perceive themselves to be discriminated against due to their membership in 10 disadvantaged groups, and if these perceptions are related to self-esteem. The findings indicate the vast majority of men and women feel discriminated against for one reason, with most indicating multiple reasons. Moreover, the findings provide support for past research indicating that perceptions of discrimination are negatively related to self-esteem.

Prisons in the Southern United States are among the most underfunded, understaffed, and crowded in the nation. This study seeks to identify how Alabama state correctional officers experienced crowding related to their mental and physical health and safety. A total of 66 correctional officers at 3 Alabama men’s prisons are surveyed about crowding in relation to job performance, health and safety, and inmate control. Respondents at all facilities, which had occupancy rates between 154% and 206% of capacity, report high levels of stress and impaired job performance due to understaffing and overwork. Officers at the most crowded prison are most stressed and fearful of inmates. In the absence of policies to reduce density or increase staffing in prisons, new strategies are urgently needed to reduce occupational stress among officers in crowded correctional facilities.

The importance of prison treatment for substance-using offenders in reducing recidivism and relapse has garnered much attention and acceptance over the past 30 years. The role of community aftercare as a continuum of the treatment process has been broadly acknowledged as essential in enhancing posttreatment success. However, our understanding of individual-level factors influencing a client’s willingness to participate and engage in aftercare remains limited. This article presents findings of individual-level factors found to be associated with successful completion of aftercare among a sample of 259 ex-offenders admitted to aftercare following 12 months of in-prison drug treatment.

Grasmick and colleagues expanded general power-control theory to include both pro-and antisocial risky behaviors more than 10 years ago; however, to date, there have been no empirical tests of their theoretical modifications. The current study tested the comprehensive model of general power-control theory using three different samples from South Korea: women who enter traditionally male-dominated occupations, female prison inmates, and women incarcerated for intimate partner killing. Results related to women’s patriarchal attitudes and preference for general risks supported our expectations and confirmed the tenets of general power-control theory that focus on both pro- and antisocial risky behaviors. In addition, the ones related to patriarchy of the family of origin supported Hagan’s original power-control theory focusing on only antisocial risky behaviors.

Entrepreneurs and entrepreneurship played a critical role in transforming Japan’s telecommunications sector. Between the mid-1990s and mid-2000s, in a sector long dominated by a stable set of large actors with well-established patterns of interaction, entrepreneurs introduced new technologies, new business models, and new norms of interaction. The subsequent transformation of Japan’s telecommunications sector was dramatic, providing consumers with not only fast and sophisticated services but also low prices and an entire new ecosystem of mobile content—a considerable departure from Japan’s long track record of being known as producer- rather than consumer-oriented, with consumers enjoying high-end services and products, but at high prices. Yet, these transformative entrepreneurs were not acting in a vacuum. Regulatory shifts in telecommunications were critical in providing opportunities for entrepreneurs, while simultaneously protecting them from large incumbent firms. These regulatory shifts were driven by the political dynamics of the 1990s as Japan struggled through its post-bubble economic malaise and political changes.

This paper quantitatively investigates the connections between the growing number of young adults in Japan who are out of the workforce—not in school, working, or seeking work—and their employment environments and family backgrounds by drawing upon six years of data from sampling studies that were conducted from 2000 to 2006. A multinomial probit model is used to generate estimates of the possible impacts of changes in local job markets and varying family characteristics on the likelihood of young adults being unemployed (not working but seeking work) or out of the workforce altogether. The paper presents the most detailed testing to date of hypotheses concerning the factors that lead young adults to leave the labor force. I find that apart from the respondents’ educational backgrounds, their parents’ employment status when the respondents were adolescents and past household income are significant determinants of young adults’ employment outcomes. Leaving the workforce is particularly correlated with previously having a high standard of living and not attaining the same level of education as one’s same sex parent.

The housing landscape of Tokyo has shifted dramatically from a housing shortage immediately after World War II, to an urban sprawl driven by the ‘salaried men's dream’ of a single-family home, to the more recent popularity of high-rise living. Tokyo as a global megacity provides a distinctive picture given its density, culture and changing economic fortunes in the postwar period. This article draws on qualitative research conducted in Tokyo among home-owning families over three generations, exploring the links between housing and family relations in the context of changing housing and labour markets. The article provides two housing ‘narratives’ highlighting different experiences and strategies of families over generations. These narratives not only capture key elements of socio-economic change in Japan since the end of the War, but also illustrate the contrasting experiences of different families, particularly in relation to the interaction between housing and employment.

This essay discusses the domestic moral and cultural reformism and the liberal expansionist discourses of leading Japanese Protestant journalists at the turn of the 20th century. It gives special attention to Uchimura Kanzo and examines his important theoretical relationships with the leading proponents of imperialism at the time, such as Tokutomi Soho, Yamaji Aizan, and Takekoshi Yosaburo. Although it is important to consider Uchimura’s religiosity and intellectual biography because they are essential to his resistance to imperial Japan, it is also necessary to compare Uchimura’s journalistic writings with those of his friends and contemporary rivals and consider them together in the context of the intellectual currents of the time. As I argue, amid developing imperialism in East Asia at the turn of the 20th century, Protestant intellectuals overall championed cosmopolitanism, promoted liberal education and international comity and ethics over jingoism, and urged sophisticated cultural development comparable to that of the West. Uchimura and other Protestants, moreover, supported liberal expansionism, that is, Japan’s expansion through peaceful and economic means in tandem with British and American imperialism and emigration overseas. Furthermore, liberal expansionism was inspired by a historicist view that the development and expansion of liberalism and capitalism would inevitably lead Japan and the rest of the world to peaceful coexistence and higher moral civilization.

Conservatism is about defending the status quo; in both Germany and Japan, the status quo is the postwar democratic regime. However, while the representatives of Germany’s intellectual conservatism have come to accept the democratic status quo of the postwar period, the same can hardly be said about intellectual conservatism in Japan, despite the two countries’ similar modern histories. This article seeks to explain this puzzle by pointing to a lack of pressure to adapt and the resulting freedom allowing Japanese conservatives to take a peculiar and consistently negative view of the postwar system’s foundations, which they do not recognize as a new tradition worth building on, but rather a deplorable ideological import forced upon Japan. Based on those observations, the article makes a case for an autonomous intellectual conservatism.

The String-to-Dictionary Matching Problem is defined, in which a string is searched for in all the possible concatenations of the elements of a given dictionary, with applications to compressed matching in variable to fixed-length encodings, such as Tunstall's. Two algorithms based on suffix trees are suggested, the one focusing on the dictionary, the other on the pattern to be searched for. The problem is then extended to deal also with patterns that include gaps. Experiments on natural language text suggest that compressed search might use less comparisons for long enough patterns, in spite of a potentially large number of encodings.

Digital forensics is a recently emerged research area, and it has attracted the attention of computer professionals, law enforcement experts and practitioners. It is a multidisciplinary area that includes multiple fields, i.e. law, computer science, finance, networking, data mining and criminal justice. We believe that papers enclosed in this Special Issue will enhance knowledge in digital forensics and will stimulate further research in the important areas of information and network security.

Dynamic voltage scaling (DVS) is one of the most effective techniques for reducing energy consumption on battery-operated embedded systems. According to the granularity of units to which voltage scaling is applied, the DVS problem can be divided into two subproblems: (i) inter-task DVS problem and (ii) intra-task DVS problem. A lot of effective DVS techniques have addressed either one of the two subproblems, but none of them have attempted to solve both simultaneously. This paper examines the problem of combined inter- and intra-task DVS, called the combined DVS (CDVS) problem. We solve the CDVS problem in two embedded system domains: one is systems with a sleep state and the other without sleep state. For systems without sleep state, we propose a close-to-optimal algorithm for the CDVS problem. We show that the algorithm is optimal when the power is a quadratically increasing function of the system's clock speed or the applied voltage level. For systems with a sleep state, we propose a refinement algorithm that fine-tunes the solution to the CDVS problem without sleep state to further reduce energy consumption by exploiting sleep state. Experimental results show that our proposed CDVS algorithm without sleep state is able to reduce the energy consumption by 12.5% on average over the results by the method that sequentially performs two existing inter- and intra-task DVS techniques, which are both optimal under no sleep state. Furthermore, our CDVS algorithm with a sleep state can reduce the energy consumption by 7.1% on average over the results by the conventional representative method that utilizes sleep state, but does not consider intra- and inter-task DVS simultaneously.

Frequent itemset mining over sliding window is an interesting problem and has a large number of applications. Sliding window is a widely used model for frequent itemset mining over data streams due to its capability to handle concept drift, its bounded memory and its low processing time. A sliding window-based algorithm requires an efficient data structure that can be updated as fast as possible when inserting and deleting transactions. Moreover, an innovative computing method is needed to provide the set of frequent patterns (FPs) with a little delay after the user issues a request for the mining results within a window. In this study, an efficient representation of the sliding window named blocked bit sequence is introduced which is aimed to store and maintain the content of the window. Moreover, by a novel technique this representation is exploited for efficiently extracting the set of FPs within the current window. Experimental evaluations on both real-life and synthetic data streams show that the proposed approach is faster than recently proposed algorithms in different phases of data stream mining.

In this paper, a new encryption scheme with three different modes of operations is proposed based on dual space-filling curves (SFSs) and a fractional wavelet transform (FrWT). This scheme is initially proposed for images and then extended to videos. The core idea behind the proposed schemes is to decompose an image/video first by the FrWT followed by the shuffling of each sub-band coefficients by means of a dual SFC. At last, an inverse FrWT is performed to get the encrypted image/video. A reliable decryption process is also proposed to construct the original image from the encrypted image. The experimental results, security and comparative analysis demonstrate the efficiency and robustness of the proposed scheme. Further, this paper also proposes an efficient implementation of an FrWT based on chaotic maps.

This paper proposes a novel high throughput internet protocol (IP) lookup engine, which is built upon a recently proposed multiple pipeline array architecture that has parallel two-dimensional circular search capabilities on intersecting and variable length pipelines. Our new engine is composed of specially designed processing elements (PEs) including dual input/output static random access memory units and bidirectional links, hence allowing search to proceed in all directions and admitting search requests from all PEs at the boundary of the array. We propose a novel data structure called compact clustered trie (CCT), which is better than traditional binary trie in terms of memory requirement and number of memory accesses. We develop novel approaches including a CCT forwarding table construction method, a mapping strategy and a suitable IP lookup algorithm. Our new lookup engine achieves a much higher average case throughput and a much lower average delay compared with existing IP lookup solutions making, for example, an 8 Tbps high-speed router front end possible. The engine is also well suited for the IPv6 addressing scheme.

Cooperative Intelligent Transportation Systems (ITS) will turn up to us with safer and more efficient driving environments as well as convenient and infotainment features for future ITS stations. ETSI and ISO are completing the standardization of the building blocks of a reference communication architecture for cooperative ITS. Future ITS stations complying with this set of standards deployed in vehicles, at the roadside infrastructures and within the Internet are expected to communicate with each other through a combination of ITS dedicated communication protocols and legacy Internet protocols. However, in spite of the wide adoption of IPv6 for cooperative ITS communications, relatively little attention has been paid to the security issues related to IPv6 signaling and IPv6 transport communications. In this paper, we present our position on the emerging and urgent IPv6-related security issues that occur in communications between ITS stations complying with the ITS station reference architecture under standardization within ETSI TC ITS and ISO TC204.

The inclusion of encryption or evasive techniques in popular applications increased the importance of characterizing network traffic based on behavior. This study aims to characterize peer-to-peer (P2P) traffic from the perspective of host computers by focusing on the packet lengths. The article explores the dissimilarities between the lengths of Internet Protocol (IP) packets generated by P2P and non-P2P applications. The heterogeneity of those lengths was assessed using entropy and compared for different classes of applications, through the implementation of a sliding analysis window. Initial observations show that the lengths of the packets generated by P2P applications are more varied than those of non-P2P applications. These patterns were used to implement a method to identify hosts running P2P applications. Unlike previous studies on this area, we used the heterogeneity of the packet lengths instead of the length value per se, and a sliding window calculation procedure was adopted to allow real-time processing. The results of this study can be used for the characterization of traffic generated by P2P applications, as well as for traffic classification and management purposes.

Under the presupposition of security, privacy and transparency, electronic voting is regarded as the next prospective development in of electoral evolution, yet it fails to command people's trust for the unsolved problems of bribery and coercion. Coercers and bribers can utilize various verification behaviors to determine their voter manipulation by confirming whether or not a voter complied with their instructions, which is making all voting systems vulnerable. Existing published methods have been inadequate because either those emphasizing practicality have not been able to achieve effective protection due to their overtly simple protection method, or others that emphasize on theories have been difficult to put into practice due to the complicated protection method devised. In this study, we propose an efficient and secure e-voting scheme that employs the password property to form a trapdoor for protecting ballot secrecy and hence withstand verification behaviors. The designed scheme could enhance the overall volition of participating in voting, as it corresponds to the three elements of information security and integrates with the required security for electronic voting, such as verifying the identity of voters and the anonymity of voting to prevent voters from being coerced and bribed, the volition of not being controlled by external factors and the scheme being able to be operated on different computers, portable devices or different platforms.

The widely used multiple pattern matching algorithms experience severe performance degradation when the number of patterns to match increases. In light of this fact, this paper presents a multi-threaded multiple pattern matching algorithm to overcome the performance degradation; this algorithm presents two additional improvements on the original Wu–Manber algorithm. First, the proposed algorithm employs a multi-threaded execution model to parallelize the pattern matching operation on multi-core processors. Second, the patterns to be searched are distributed over multiple threads according to the pattern similarity. For this purpose, the proposed algorithm groups the target patterns on the basis of their suffixes and distributes the patterns over multiple threads. Through experiments and performance analysis, our algorithm shows a significant performance gain as compared with the original Wu–Manber algorithm and the previously proposed multi-threaded pattern matching on massive pattern sets of size exceeding 5000. The results obtained from the pattern matching operation using eight cores show much improved execution time, which is nearly 14.9 times faster on average than that of the conventional Wu–Manber algorithm. It is demonstrated that the proposed idea improves the overall performance by reducing the amount of workload on a single thread through multi-threading and an efficient data distribution policy.

We proposed two multiparty quantum secret sharing schemes based on n-particle Greenberger–Horne–Zeilinger-states (GHZ states), which are transformed from Einstein–Podolsky–Rosen pairs by entanglement swapping. In our schemes, the dealer imposes messages by performing local unitary operations (I, x, iy, z) on the n-particle GHZ state she holds, and the agents collaborate to deduce the dealer's messages by performing local unitary operations on their own qubit. The amount of dealer's secret message is positively related with the number of agents. The need of qubits is one-third less than the former schemes and, also, they can be reused for the next new round. The scheme (II) does not have to pre-share the code table, which increases the security without the risk of being stolen.

High feature dimensionality of realistic datasets adversely affects the recognition accuracy of nearest neighbor (NN) classifiers. To address this issue, we introduce a nearest feature classifier that shifts the NN concept from the global-decision level to the level of individual features. Performance comparisons with 12 instance-based classifiers on 13 benchmark University of California Irvine classification datasets show average improvements of 6 and 3.5% in recognition accuracy and area under curve performance measures, respectively. The statistical significance of the observed performance improvements is verified by the Friedman test and by the post hoc Bonferroni–Dunn test. In addition, the application of the classifier is demonstrated on face recognition databases, a character recognition database and medical diagnosis problems for binary and multi-class diagnosis on databases including morphological and gene expression features.

Many scientific experiments are carried out in collaboration with researchers around the world to use existing infrastructures and conduct experiments at massive scale. Data produced by such experiments are thus replicated and cached at multiple geographic locations. This gives rise to new challenges when selecting distributed data and compute resources so that the execution of applications is time- and cost-efficient. Existing heuristic techniques select ‘best’ data source for retrieving data to a compute resource and subsequently process task-resource assignment. However, this approach of scheduling, which is based only on single source data retrieval, may not give time-efficient schedules when: (i) tasks are interdependent on data, (ii) the average size of data processed by most tasks is large and (iii) data transfer time exceeds task computation time by at least one order of magnitude. In order to address these characteristics of data-intensive applications, we propose to leverage the presence of replicated data sources, retrieve data in parallel from multiple locations and thus achieve time-efficient schedules. In this article, we propose two multi-source data-retrieval-based scheduling heuristic that assigns interdependent tasks to compute resources based on both data retrieval time and task-computation time. We carry out experiments using real applications and deploy them on emulated as well as real environments. With a combination of data retrieval and task-resource mapping technique, we show that our heuristic produces time-efficient schedules that are better than existing heuristic-based techniques for scheduling application workflows.

The Capsule Reviews are intended to provide a short succinct review of each paper in the issue in order to bring it to a wider readership. The Capsule Reviews were compiled by Fairouz Kamareddine. Professor Kamareddine is an Associate Editor of The Computer Journal and is based in the Department of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh, UK.

In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P). Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures.

The NAS Parallel Benchmarks (NPB) are well-known applications with fixed algorithms for evaluating parallel systems and tools. Multicore clusters provide a natural programming paradigm for hybrid programs, whereby OpenMP can be used with the data sharing with the multicores that comprise a node, and MPI can be used with the communication between nodes. In this paper, we use Scalar Pentadiagonal (SP) and Block Tridiagonal (BT) benchmarks of MPI NPB 3.3 as a basis for a comparative approach to implement hybrid MPI/OpenMP versions of SP and BT. In particular, we can compare the performance of the hybrid SP and BT with the MPI counterparts on large-scale multicore clusters, Intrepid (BlueGene/P) at Argonne National Laboratory and Jaguar (Cray XT4/5) at Oak Ridge National Laboratory. Our performance results indicate that the hybrid SP outperforms the MPI SP by up to 20.76%, and the hybrid BT outperforms the MPI BT by up to 8.58% on up to 10 000 cores on Intrepid and Jaguar. We also use performance tools and MPI trace libraries available on these clusters to further investigate the performance characteristics of the hybrid SP and BT.

This paper presents a benchmarking, performance analysis and optimization study of the OP2 ‘active’ library, which provides an abstraction framework for the parallel execution of unstructured mesh applications. OP2 aims to decouple the scientific specification of the application from its parallel implementation, and thereby achieve code longevity and near-optimal performance through re-targeting the application to execute on different multi-core/many-core hardware. Runtime performance results are presented for a representative unstructured mesh application on a variety of many-core processor systems, including traditional X86 architectures from Intel (Xeon based on the older Penryn and current Nehalem micro-architectures) and GPU offerings from NVIDIA (GTX260, Tesla C2050). Our analysis demonstrates the contrasting performance between the use of CPU (OpenMP) and GPU (CUDA) parallel implementations for the solution of an industrial-sized unstructured mesh consisting of about 1.5 million edges. Results show the significance of choosing the correct partition and thread-block configuration, the factors limiting the GPU performance and insights into optimizations for improved performance.

In this paper, we describe the integrated power, area and thermal modeling framework in the structural simulation toolkit (SST) for large-scale high performance computer simulation. It integrates various power and thermal modeling tools and computes run-time energy dissipation for core, network on chip, memory controller and shared cache. It also provides functionality to update the leakage power as temperature changes. We illustrate the utilization of the framework by applying it to explore interconnect options in manycore systems with consideration of temperature variation and leakage feedback. We compare power, energy-delay-area product (EDAP) and energy-delay product (EDP) of four manycore configurations-1 core, 2 cores, 4 cores and 8 cores per cluster. Results from simulation with or without consideration of temperature variation both show that the 4-core per cluster configuration has the best EDAP and EDP. Even so, considering that temperature variation increases total power dissipation, we demonstrate the importance of considering temperature variation in the design flow. With this power, area and thermal modeling capability, the SST can be used for hardware/software co-design of future exascale systems.

With the advent of heterogeneous computing systems consisting of multi-core central processing units (CPUs) and many-core graphics processing units (GPUs), robust methods are needed to facilitate fair benchmark comparisons between different systems. In this paper, we present a benchmarking methodology for measuring a number of performance metrics for heterogeneous systems. Methods for comparing performance and energy efficiency are included. Consideration is given to further metrics, such as associated running costs and carbon emissions. We give a case study for these metrics applied to Bristol University Docking engine, a molecular mechanics-based docking application that has been ported to open computing language at the University of Bristol. Results are included for both AMD and NVIDIA GPUs, and for a highly optimized code on the latest x86 CPUs.

Concurrency levels in large-scale supercomputers are rising exponentially, and shared-memory nodes with hundreds of cores and non-uniform memory access latencies are expected within the next decade. However, even current petascale systems with tens of cores per node suffer from memory bottlenecks. As core counts increase, memory issues will become critical for the performance of large-scale supercomputers. Trace analysis tools are thus vital for diagnosing the root causes of memory problems. However, existing memory tracing tools are expensive due to prohibitively large trace sizes, or they collect only statistical summaries and omit potentially valuable information. In this paper, we present ScalaMemTrace, a novel technique for collecting memory traces in a scalable manner. ScalaMemTrace builds on prior trace methods with aggressive compression techniques to allow lossless representation of memory traces for dense algebraic kernels, with near-constant trace size irrespective of the problem size or the number of threads. We further introduce a replay mechanism for ScalaMemTrace traces, and discuss the results of our prototype implementation on the x86_64 architecture.

This paper advances a novel approach that facilitates the location of services and/or digital assets advertised by directories in a Mobile Ad hoc Network. The proposed Service Directory Placement Protocol (SDPP) improves scalability and reduces packet traffic overhead by advancing a multi-directory extension of an earlier approach that relied on the migration of a single directory through the network. This investigation demonstrates that modelling the directory replication problem as a Semi-Markov Decision Problem solved by means of a Reinforcement Learning technique known as Q-learning improves the performance of SDPP. Computer simulations validate the feasibility of the proposed scheme that enables packet overhead reductions between 15 and 75 %, whereas the directory location success rate improves by up to 11% when compared with pure broadcast flooding and other existing approaches in wireless networks where hosts move at walking speeds.

Java embedded systems often include Java middleware classes installed on the client device. For higher performance, they can be compiled into machine code before runtime using an ahead-of-time compiler (AOTC). There are many approaches to AOTC, yet a bytecode-to-C (b-to-C) AOTC which translates the bytecode into the C code and then compiles it using an existing optimizing compiler such as gcc would be the most straightforward one. This paper explores a few important design and optimization issues of a b-to-C AOTC, including the compilation form for the translated C code, the call interfaces among translated and interpreted Java methods, and Java-specific optimizations by the AOTC that can complement the gcc optimizations. We evaluate these issues with our b-to-C AOTC implemented on the MIPS platform for the Sun's CDC VM to understand their performance impact.