The changesets (or patches) that fix open source software vulnerabilities form critical datasets for various machine learning security-enhancing applications, such as automated vulnerability patching and silent fix detection. These patch datasets are derived from extensive collections of historical vulnerability fixes, maintained in databases like the Common Vulnerabilities and Exposures list and the National Vulnerability Database. However, since these databases focus on rapid notification to the security community, they contain significant inaccuracies and omissions that have a negative impact on downstream software security quality assurance tasks.In this paper, we propose an approach employing Uncertainty Quantification (UQ) to curate datasets of publicly-available software vulnerability patches. Our methodology leverages machine learning models that incorporate UQ to differentiate between patches based on their potential utility. We begin by evaluating a number of popular UQ techniques, including Vanilla, Monte Carlo Dropout, and Model Ensemble, as well as homoscedastic and heteroscedastic models of noise. Our findings indicate that Model Ensemble and heteroscedastic models are the best choices for vulnerability patch datasets. Based on these UQ modeling choices, we propose a heuristic that uses UQ to filter out lower quality instances and select instances with high utility value from the vulnerability dataset. Using our approach, we observe an improvement in predictive performance and a significant reduction of model training time (i.e., energy consumption) for a state-of-the-art vulnerability prediction model.

This research paper describes a study of using real-world vulnerabilities to motivate computer science students to-wards learning secure programming. Given the rise in cybersecurity incidents due to programming errors, there is a pressing need to improve programmers' secure programming skills. Despite educators' numerous efforts towards this goal, communicating the importance of this training to students remains a challenge. Grounding on the theory of intrinsic motivation, we propose that exposing students to authentic, relatable vulnerabilities can significantly enhance their learning orientation towards secure programming. Our approach involves selecting vulnerabilities from the National Vulnerability Database that are both relatable to students and understandable without extensive external context. These vulnerabilities are transformed into comprehensive course modules, each featuring a demonstrative video, source code snippets of the vulnerability and its patch, and associated developer communications about the vulnerability. We assess the impact of one of our course modules on students' learning disposition through a study conducted in two universities in an identical setting. The study results indicate that students appreciate seeing real-world vulnerabilities in detail, especially the video we recorded reproducing the vulnerability, and that they gain in self-efficacy after completing the module.

Reflection is known to be an effective means to improve students' learning. In this paper, we aim to foster meaningful reflection via prompts in computer science courses with a significant practical, software development component. To this end we develop an instructional strategy and system that automatically delivers prompts to students based on their commits in a source code repository. The system allows for prompts that instigate reflection in students to be timely with respect to students' work, and delivered automatically, thus easily scaling up the strategy.

Interaction data, gathered from developers' daily clicks and key presses in the IDE, has found use in both empirical studies and in recommendation systems for software engineering. We observe that this data has several characteristics, common across IDEs:

While early software command recommender systems drew negative user reaction, recent studies show that users of unusually complex applications will accept and utilize command recommendations. Given this new interest, more than a decade after first attempts, both the recommendation generation (backend) and the user experience (frontend) should be revisited. In this work, we focus on recommendation generation. One shortcoming of existing command recommenders is that algorithms focus primarily on mirroring the short-term past,-i.e., assuming that a developer who is currently debugging will continue to debug endlessly. We propose an approach to improve on the state of the art by modeling future task context to make better recommendations to developers. That is, the approach can predict that a developer who is currently debugging may continue to debug OR may edit their program. To predict future development commands, we applied Temporal Latent Dirichlet Allocation, a topic model used primarily for natural language, to software development interaction data (i.e., command streams). We evaluated this approach on two large interaction datasets for two different IDEs, Microsoft Visual Studio and ABB Robot Studio. Our evaluation shows that this is a promising approach for both predicting future IDE commands and producing empirically-interpretable observations.

Many security models and systems are based on the assumption that super users must be trusted. It is difficult to hold super users accountable because they can erase any logs of their activities and impersonate as other users. This work proposes an accountable system administration model for operating systems where the notion of super users is removed and all system administrators must be accounted for their activities even if they are untrustworthy. The model is built upon a premise that such a system has multiple peer system administrators, and the peer system administrators ensure the logs of their activities are preserved and audited. The accountability policy and operating system primitives are designed and constructed so that the proposed model is provable. An enforcement mechanism that instantiates the model and enforces the policy is designed and implemented in Linux, a real-world operating system.

Using IDE usage data to analyze the behavior of software developers in the field, during the course of their daily work, can lend support to (or dispute) laboratory studies of developers. This paper describes a technique that leverages Hidden Markov Models (HMMs) as a means of mining high-level developer behavior from low-level IDE interaction traces of many developers in the field. HMMs use dual stochastic processes to model higher-level hidden behavior using observable input sequences of events. We propose an interactive approach of mining interpretable HMMs, based on guiding a human expert in building a high quality HMM in an iterative, one state at a time, manner. The final result is a model that is both representative of the field data and captures the field phenomena of interest. We apply our HMM construction approach to study debugging behavior, using a large IDE interaction dataset collected from nearly 200 developers at ABB, Inc. Our results highlight the different modes and constituent actions in debugging, exhibited by the developers in our dataset.

Abstract Logging has become a fundamental feature within the modern computer operating systems because of the fact that logging may be used through a variety of applications and fashion, such as system tuning, auditing, and intrusion detection systems. Syslog daemon is the logging implementation in Unix/Linux platforms, while Windows Event Log is the logging implementation in Microsoft Windows platforms. These logging implementations provide application program interfaces that, in turn, simplify logging functions from data collection to data storage. In this paper, we survey Unix, Linux, and Windows logging mechanisms and introduce their security issues. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, study how to achieve accountable logging for operating system using the flow-net logging and its implementation in current operating system such as Linux. We demonstrate that the flow-net logging technique is capable of preserving event relationship. The performance for the flow-net logging implementation in Linux operation system is evaluated.

Logging is a critical component of Linux auditing. The experiments indicate that the logging overhead can be significant. The paper aims to leverage the performance overhead introduced by Linux audit framework under various usage patterns. The study on the problem leads an adaptive audit logging mechanism. The adaptive auditing mechanism reduces the overall system overhead and achieves a similar level of protection on the system and network security.

Abstract Logging is a critical component of Linux auditing. However, our experiments indicate that the logging overhead can be significant. The paper aims to leverage the performance overhead introduced by Linux audit framework under various usage patterns. The study on the problem leads to an adaptive audit‐logging mechanism. Many security incidents or other important events are often accompanied with precursory events. We identify important precursory events – the vital signs of system activity and the audit events that must be recorded. We then design an adaptive auditing mechanism that increases or reduces the type of events collected and the frequency of events collected based upon the online analysis of the vital‐sign events. The adaptive auditing mechanism reduces the overall system overhead and achieves a similar level of protection on the system and network security. We further adopt LMbench to evaluate the performance of key operations in Linux with compliance to four security standards. Copyright © 2015 John Wiley & Sons, Ltd.

This paper concerns teaching computer science undergraduate students to develop sophisticated sensor-driven mobile applications, which students find interesting and motivating. Computer science students commonly adopt a trial-and-error application development process. However, indeterminacy inherent in sensor data makes the trial-and-error approach difficult, which frustrates students and impairs learning. In addition, the complexity of modern mobile devices' development environment and numerous APIs can further undo the motivating effect that these types of applications bring. To address these challenges, we propose a teaching model for sensor-driven mobile application development. The model features an application development process and a set of supporting tools and programs. The model provides a structured way for students to deal with the indeterminacy of sensor data and the complex development environments and results in a positive and supportive learning experience for the students. A case study of applying the model in an upper-level computer science elective course has shown it to be effective.

The greater versatility and increasingly smaller sizes of computing, sensing, and networking devices have resulted in a new computing paradigm called Cyber-Physical Systems (CPSs), which integrates computation and sensing into physical processes producing a wealth of exciting applications in many domains of life, such as transportation, medicine, and agriculture. In order to equip students with the essential knowledge and skills to be successful in the future, this paradigm requires an expansion in the scope of computer science curricula to enable students to understand and overcome the complexity inherent in CPSs. In this paper, we describe our experience with teaching CPS via a set of course modules that rely heavily on modeling and verification. By using the popular Android platform, we aim to engage students to successfully build CPS applications while enhancing their understanding of intellectually challenging concepts.

We study load balancing of paging schemes for multitier hierarchical cellular networks, in which different tiers of cells overlay each other to provide multiple coverage in cellular service areas. Each mobile terminal (MT) can be paged in any tier of a multitier hierarchical cellular network. Paging requests are balanced in different waiting queues of different tiers, and the load balancing among them is achieved probabilistically among N tiers. The studied paging schemes are the Hierarchical Pipeline Paging scheme, the Hierarchical Sequential Paging scheme, and the Hierarchical Blanket Paging scheme. We study two optimization problems using the N-tier load balancing: 1) given a paging delay constraint, to minimize the total paging cost under the constraint that the total delay is upper bounded by a predefined total delay, and 2) given a bound on the total delay, to minimize the total paging cost under a paging delay constraint.

Many security models and systems are built upon the assumption that super users are trustworthy. However, it becomes challenging to hold super users accountable since they can erase any trace of their activities. This paper proposes an accountable administration model for operating systems where all system administrators can be accounted for even if they are untrustworthy. The model is implemented in Linux, a real world operating system.

In wireless sensor networks, some sensor nodes are put in sleep mode while other sensor nodes are in active mode for sensing and communication tasks in order to reduce energy consumption and extend network lifetime. This approach is a special case (k=2) of a randomized scheduling algorithm, in which k subsets of sensors work alternatively. In this paper, we first study the randomized scheduling algorithm via both analysis and simulations in terms of network coverage intensity, detection delay, and detection probability. We further study asymptotic coverage and other properties. Finally, we analyze a problem of maximizing network lifetime under quality of service constraints such as bounded detection delay, detection probability, and network coverage intensity. We prove that the optimal solution exists, and provide conditions of the existence of the optimal solutions.

Abstract Anonymity is an important security aspect of wireless communications and has continuously attracted significant attention. Implementing anonymity of mobile users not only protects their privacy but also reduces the chances of attacks based on impersonation; therefore security can be improved. Untraceability is a related issue to anonymity. If a user is traceable, its hidden identity can be revealed through profiling the activities associated to a user. In this paper, we conduct a survey on anonymity issues of wireless communication systems. We first discuss general issues of anonymity in wireless communication systems. Then we survey some protocols in the literature, which are designed for wireless mobile systems as well as wirelessad hocnetworks. Copyright © 2008 John Wiley & Sons, Ltd.

We propose a hierarchical pipeline paging (HPP) for multi-tier hierarchical cellular networks, in which different tiers overlay with one another to provide overlapped coverage of cellular service, and each mobile terminal can be paged in any tier of a network. Paging requests (PRs) are queued in different waiting queues, and multiple PRs in each waiting queue are served in a pipeline manner. We study HPP, hierarchical sequential paging (HSP), and hierarchical blanket paging (HBP) schemes analytically in terms of discovery rate, total delay, paging delay, and cost. It is shown that HPP scheme outperforms both HBP and HSP schemes in terms of discovery rate while maintaining the same cost as HSP scheme. The HPP scheme outperforms HSP scheme in terms of total delay and has a lower total delay than HBP scheme when traffic load is high.

In this paper, we carry out a comprehensive study to compare invalidation report (IR) to three other cache access algorithms, including poll-each-read (PER), call-back (CB), and lease schemes in future multiple cell wireless Internet. The purpose of this study is to study scalability of IR schemes. To the best of our knowledge, this is the first such study. We focus on the scalability issue of these four fundamental strong-consistent schemes in terms of network transmission costs regarding network size, database size, subscription ratio, and network traffic through extensive computer simulations. Our results show that: 1) the IR schemes do not perform well in multiple-cell wireless Internet with a large update rate, database size, and IR window size, and with a small subscription density; 2) the IR schemes do scale up well with the IR period and the access rate; however, good performance can be obtained when the IR period is large, which implies a large access latency; 3) the PER CB and the lease schemes perform well with a large update rate, small subscription ratio, and large database size.

In this paper, we provide a performance evaluation for blanket paging scheme, sequential probability paging scheme, and pipeline probability paging scheme in wireless networks. Both analytical models and extensive simulations are adopted to study these schemes.

In our previous work [11], we derived detection delay and detection probability for a randomized scheduling algorithm in wireless sensor networks. In this paper, we study asymptotic coverage, prove many mathematical lemmas, and study properties including asymptotic properties of network coverage intensity, detection probability, and detection delay in wireless sensor networks.

A dynamic periodic location area update (DPLAU) scheme was proposed for 3GPP technical specifications for the circuit-switched domain of universal mobile telecommunications system. In this paper, we propose an analytical model for the optimal DPLAU (ODPLAU) scheme to minimize the cost of location management under the presence of abnormal detachments. Simulations are conducted and validate the analytics results.

In wireless sensor networks, in order to minimize energy consumption and extend network lifetime, some sensors are put in the sleep mode while the other sensor nodes are in the active mode for the sensing and communication tasks. In a randomized scheduling algorithm, a set of sensors work alternatively. In this paper, we provide an analytical model for the randomized scheduling algorithm, and derive detection delay and detection probability. Simulations are conducted to validate analytical results.

Cache can be used for mobile devices to reduce the usage of limited bandwidth in wireless networks. Ideally, frequently accessed and infrequently updated data items should be cached and infrequently accessed and frequently updated data items should be evicted or not cached at all. Most of the existing cache replacement policies adopt only access information so that frequently updated data items are also cached. As a remedy, we propose a cache replacement policy, called On-Bound Selection (OBS), that uses both data access and update information. The proposed OBS is inspired by an analytical analysis for a server-based Poll-Each-Read (SB-PER) and a revised Call-Back (R-CB). The OBS provides an upper bound for effective hit ratio and a lower bound for communication cost. The proposed scheme is evaluated and compared with a least frequently used (LFU) replacement policy through extensive simulations. Simulation results show that the OBS outperforms LFU in terms of both effective hit ratio and communication cost.

Mobility databases such as home location register and visitor location register are adopted to support mobility management in personal communications services networks. If a visitor location register fails or crashes, the subscribers' services will be seriously degraded due to the loss or corruption of location information. In this paper, we optimize utilization of demand re-registration messages for an adaptivep-persistent backoff database failure restoration scheme. An analytical model is developed and validated with simulations to obtain the optimal utilization using appropriate parameters so that the failed visitor location register is restored with the fastest speed. Some interesting aspects on the performance are studied and their deep insights are observed, such as effects of message sizes on choices of system parameters, effects of the inaccurate estimated number of stations, etc. One observation is that optimizations of utilization and successful transmission probability are two different goals, and a value to achieve the optimal successful transmission probability does not necessarily ensure optimal utilization. Furthermore, we also propose a scheme how to handle the problem with inaccurate (estimated) number of stations.

Paging schemes for wireless systems have been well studied in the literature. However, most schemes are considered on per user basis. In these schemes, when an incoming call arrives at a mobile terminal (MT), a paging request (PR) is put in a queue. PRs are served in an FIFO manner. When a PR is served, a search process is carried out to find the corresponding MT in a location area (LA). Most schemes study how to achieve a better performance in terms of cost with/without delay constraints per PR, and totally ignore other PRs in the queue until the MT is found or all the cells in the LA have been paged. In this paper, we propose a non-blocking pipeline probability paging scheme, which assumes known knowledge on location probabilities of individual MTs, under a paging delay constraint, where the location probability of an MT in a cell is the probability that the MT is in the cell. The proposed scheme is independent of the number of PRs in the queue and the arrival rate of PRs. Our study shows that the proposed scheme outperforms both the sequential probability paging scheme with known knowledge on location probabilities of individual MTs and the blanket paging scheme in terms of discovery rate and the total delay. Finally, we study several optimization problems with quality of service constraint for the pipeline probability paging scheme.

Most of existing cache replacement policies are access-based replacement policies where update process is ignored. However, update information is extremely important. In this paper, we provide a deep analysis on cache access algorithms, and propose a step-wise optimal update-based replacement policy, called update-based step-wise optimal (USO) scheme, to optimize transmission cost and effective hit ratio at each replacement. Unlike traditional studies of replacement policies which are mostly based on only intuitions, our proposed scheme is based on quantitative analysis, and optimality is proved by an analytical model. The extensive simulations have shown that the advantage of the proposed replacement policy.

In this paper, we compare a pipeline probability paging scheme, a blanket paging scheme, and a sequential probability paging scheme in wireless networks. An optimization problem under quality of service constraint is studied for the pipeline probability paging scheme.

In this paper, we study a randomized scheduling algorithm, and analyze the problem of maximizing network lifetime under quality of service constraints such as bounded values of detection delay, detection probability, and network coverage intensity in wireless sensor networks. We show that the optimal solutions exist and provide the conditions of the existence of the optimal solutions.

In this paper, we propose and study a hierarchical pipeline paging (HPP) for multi-tier hierarchical cellular networks, in which each mobile terminal (MT) can be paged in any tier of a network. Furthermore, paging requests are queued in N different waiting queues, where N stands for the number of tiers, and multiple paging requests in each waiting queue are served in a pipeline manner. We study the HPP scheme analytically in terms of discovery rate, total delay, paging delay, cost, and load balance, validated with simulations.

Cache can be used for mobile devices to reduce the usage of scarce wireless channels in wireless networks. Ideally, only frequently accessed and infrequently updated data items should be cached and infrequently accessed and frequently updated data items should be evicted or not cached at all. Existing cache replacement policies which use only access information may cause frequently updated data items to be cached. As a remedy, we propose a cache replacement policy, called asymptotical keep-best (AKB), that uses both data access and update information. The proposed AKB is evaluated and compared with the least recently used replacement policy (LFU) through extensive simulations. Simulation results show that the proposed replacement policy outperforms LFU in terms of both effective hit ratio and communication cost.

In this paper, we compare the normal location area update (NLAU) scheme, the periodic location area update (PLAU) scheme, and the PNLAU (NLAU+PLAU) scheme in 3GPP specifications for Universal Mobile Telecommunications System (UMTS) in terms of signaling and initial trunk setup cost. We analytically model cost functions of these schemes per checkpoint event. Optimality issue of the PNLAU has been studied to minimize the total cost of signaling and failure call setup, and optimal values are derived.

In this paper, we propose a pipeline probability paging to reduce delay and improve performance for wireless systems assuming prior knowledge on location probabilities of individual mobile terminals, under a paging delay constraint. Our study shows that the proposed scheme outperforms both the sequential probability paging scheme with prior knowledge on location probabilities of individual mobile terminals and the blanket paging scheme in terms of discovery rate and the total delay.

Cache has been applied for wireless data access with different replacement policies in wireless networks. Most of the current cache replacement schemes are access-based replacement policies since they are based on object access frequency/recency information. Access-based replacement policies either ignore or do not focus on update information. However, update information is extremely important since it can make access information almost useless. In this paper, we consider two fundamental and strongly consistent access algorithms: Poll-Per-Read (PER) and Call-Back (CB). We propose a server-based PER (SB-PER) cache access mechanism in which the server makes replacement decisions and a client-based CB cache access mechanism in which clients make replacement decisions. Both mechanisms have been designed to be suitable for using both update frequency and access frequency. We further propose two update-based replacement policies, least access-to-update ratio (LA2U) and least access-to-update difference (LAUD). We provide a thorough performance analysis via extensive simulations for evaluating these algorithms in terms of access rate, update rate, cache size, database size, object size, etc. Our study shows that although effective hit ratio is a better metric than cache hit ratio, it is a worse metric than transmission cost, and a higher effective hit ratio does not always mean a lower cost. In addition, the proposed SB-PER mechanism is better than the original PER algorithm in terms of effective hit ratio and cost, and the update-based policies outperform access-based policies in most cases.

Strongly consistent callback cache mechanisms have been studied for data access in wireless networks. In cache access mechanisms, update information is extremely important since an updated data object in a remote server makes the corresponding data objects invalidated in mobile terminals (MTs), and the data object cache hit information in those MTs becomes almost useless. In this paper, we propose an adaptive access mechanism, called optimal callback with two-level adaptation. In the first-level adaptation, cache size in an MT is adaptively adjusted based on update-to-access-ratio (UAR), defined as the average number of updates per data object access. The range of the cache size is [O, M], where M is the maximum physical cache size of the MT. Two extreme cases are given as follows: 1) when the UAR is very large so that objects in the cache are always obsolete, the cache should not be used and, therefore, the cache size should be set to zero; 2) when the UAR is zero so that every object in the cache is valid, the cache size should be set to M. Under other situations, the cache size is dynamically changed between O and M. Define U-threshold of the UAR for any object, a particular important threshold, as a UAR value, beyond which the object should be not cached at all. The idea of the second-level adaptation is that if an object size is small, sending back the object may be a better choice than sending back an invalidation message when the object is updated. Therefore, when an object is updated at the server, it is sent directly to MTs if the object size is smaller than a threshold, called push threshold (T); otherwise, an invalidation message is sent to the MTs. We analytically model cost function for the proposed adaptive scheme as the total traffic involved between the server and an MT per data object access, and the optimal cache size and the optimal T value are obtained simultaneously to minimize the cost function. Furthermore, U-threshold is derived analytically. Both simulations and analytical results are used to study and compare the performance of the proposed scheme with several others under many different scenarios.

In this paper, we present a simple pipeline paging (PP) scheme, in which multiple paging requests (PRs) can be served in a pipeline manner in different paging areas. We analytically model the blanket paging (BP) scheme, the sequential paging (SP) scheme, and the PIP scheme so that discovery rate, total delay, paging delay, and cost are derived analytically as functions of traffic load. Extensive simulations are carried out to verity our analytical results. Our study shows that the PIP scheme outperforms both the BIP and SIP schemes in terms of discover rate while maintaining the same cost as the SIP scheme. The PIP scheme outperforms the SP scheme in terms of total delay and has a lower total delay than the BIP scheme when traffic load is high. We also show that, when the paging delay constraint D is large enough, the PIP scheme achieves almost 200 percent of discovery rate and 50 percent of cost of the BP scheme, whereas discovery rate of the SIP scheme is far less than that of the BP scheme. Furthermore, we solve the following two-optimization problems for the PIP scheme: 1) the minimization of discovery rate with a bound on total delay and 2) the minimization of cost with a bound on total delay. In case the cost factor is not considered but total delay is important, we propose an adaptive scheme: When the traffic is lower than a threshold, the BIP scheme is adopted; otherwise, the PIP scheme is used. In this case, the threshold value is explicitly derived.

A normal location area update (NLAU) and a periodic location area update (PLAU) schemes are adopted in 3GPP specifications for Universal Mobile Telecommunications System to detect presence of mobile stations. In this paper, we compare the NLAU scheme, the PNLAU (NLAU+PLAU) scheme, and a dynamic PNLAU scheme in terms of signaling and initial trunk setup cost. We analytically model cost functions of these schemes per checkpoint event. Optimality issues of the PNLAU have been studied, and optimal values are derived. The first optimality issue is to minimize the total cost of signaling and failure call setup. The second optimality issue is to minimize the signaling cost with an upper bound on failure call setup probability. Simulations are carried out to validate against analytical results.

Most of cache replacement schemes are access-based replacement policies since they are based on object access frequency/time information. However, update information is extremely important since an updated object makes itself invalid, and the object hit information becomes useless. In this paper, we propose two update-based replacement policies, the least access-to-update ratio (LA2U) and least access-to-update difference (LAUD) in wireless data access, based on both update frequency and access frequency. Extensive simulations have been carried out to evaluate the proposed policies. Simulation results show that the proposed update-based policies outperform access-based policies at most cases. It is concluded that considering update information in designing replacement policies can increase cache performance, especially, when updates are heavy.

Cache mechanisms have been proposed for wireless data access. Poll-each-read (PER) is a fundamental cache access algorithm and has been studied in wireless data access. However, PER overlooks the importance of update information. In this paper, we propose a server-based PER (SB-PER) cache access mechanism in which the server makes replacement decisions. Through extensive simulations, we provide a profound performance analysis for the SB-PER in terms of access rate, update rate, cache size, and database size, which is useful for understanding of the related algorithms. Simulation results show that the proposed SB-PER outperforms the original PER in terms of effective hit ratio and cost.

We propose a two-level adaptive callback access mechanism for wireless data access. In the first level, cache size in a mobile termination is adaptively adjusted based on update-to-access-ratio. In the second level adaptation, when an object is updated at the server, whether to send the object directly or an invalidation message, adaptively depends on the object size. We analytically model cost function, and the optimal cache size and the optimal adaptation threshold value are obtained simultaneously. Both simulations and analytical results are used to study the performance.

In this paper, we present analytical models for the pipeline paging (PP) scheme, the blanket paging (BP) scheme, and the sequential paging (SP) scheme. In the PP scheme, multiple paging requests can be served in a pipeline manner in different paging areas. Discovery rate, total delay, paging delay, and cost are derived analytically as functions of traffic load. Extensive simulations are carried out to verify our analytical results. Our study shows that the PP scheme outperforms both the BP and SP schemes.

In sequential paging (SP) schemes, the paging process is considered on a per user basis. When an incoming call reaches a mobile terminal (MT), the associated location area is divided into several paging areas (PAs) and PAs are paged one by one until the MT is found. Even though SP algorithms can reduce the paging cost compared to blanket paging (BP), they introduce extra and unnecessary delay, and are not efficient. We present a pipeline paging (PP) scheme in which multiple paging requests (PRs) can be served in a pipeline manner for different paging areas. We study the proposed scheme via extensive simulations in terms of discovery rate, total delay, and cost under different traffic loads. Our study shows that the PP scheme outperforms both the BP and SP schemes in terms of discover rate and total delay, while maintaining a cost similar to that of the SP scheme. The study also shows that when the paging delay constraint D is as large as 6, the PP scheme achieves almost 171% of the BP's discovery rate and 58% of the BP's cost, whereas the SP's discovery rate is far less than that of the BP scheme.