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Proof-of-Work (PoW) blockchain systems like Bitcoin and Ethereum are vulnerable to selfholding attack. The prior modeling-based works about this attack only considered Bitcoin and assumed that there were at most two honest pools in a perfect network (no natural fork in such networks). However, a blockchain network is imperfect due to block propagation delay, which can lead to forking. Moreover, there may be more than two pools under attack. This paper aims for a quantitative analysis of an imperfect PoW blockchain network system under selfholding attack. We develop a novel stochastic model and derive formulas to evaluate the effect of selfholding attack on miner revenue, system security and system performance. Our work can be used to analyze the scenario where there are any number of pools suffering selfholding attack in both Ethereum and Bitcoin. The model in this paper can capture the behaviors of a more realistic and more general scenario, compared with the existing models. Moreover, our model and formulas can also be applied to evaluate a blockchain system, which uses a similar reward mechanism and is vulnerable to selfholding attack. Our work can help design a more secure blockchain incentive mechanism and an in-pool reward mechanism.

Selfish mining, as a serious threat to blockchain, has been attracting attentions from academic and industry. Stochastic modeling has been explored to quantitatively investigate selfish mining in imperfect blockchain networks. However, prior modeling-based analysis approaches have some of the following issues: (1) only focus on Bitcoin or Ethereum, or (2) ignore extended forks and just consider natural forks, or (3) only compute the mining revenue without assessing the performance and security of the blockchain system when the system suffers from selfish mining. In this paper, we aim to address these issues. We build a Markov chain to make quantitative analysis of selfish mining in imperfect Bitcoin and Ethereum networks with natural and extended forks. Formulas are derived to calculate the mining revenue for the selfish pool (comprising selfish miners) and honest miners, respectively. Moreover, we derive the formulas of performance metrics (namely, transactions per second and stale block ratio) and the formula of security metric (namely, the probability of double-spending success) of the system. These quantitative results can help understand the impact of selfish mining on imperfect blockchain networks and then help the detection of selfish mining.

This work proposes Practical Byzantine Fault Tolerance (PBFT) ordering service needed for block formation in permissioned blockchain environments. Contrary to current PBFT implementations that only provide a single point of entry to the ordering service, we allow each ordering node to act as an entry point that proposes and conducts the consensus process of including new record in the distributed ledger. To ensure atomicity of record insertion in distributed ledger, we have developed a bandwidth reservation protocol that uses a modification of CSMA/CA protocol to regulate access to the broadcast medium formed by the P2P network of TCP connections between orderers. We have modeled record insertion service time in a cluster where ordering nodes have random position within Cartesian coordinate system. We have also modeled total request access time to the ledger which includes waiting time in the orderer's queue and record insertion time. These models are used to evaluate system performance under variable request rate ordering service, variable number of nodes and variable physical cluster dimensions. Our results show the interaction between decreased request waiting time in orderer's queue and increased contention among orderers when the number of orderers increases for the given total request arrival rate. This interaction is also investigated for two different physical cluster sizes which affect record insertion time. The interplay of request rate, number of orderers and physical cluster size determines system capacity expressed in total request rate. Our model can be used to make the trade-off between the required system capacity, number of orderers, and physical cluster dimensions under constraints on Byzantine fault rate.

Selfish mining strategy allows miners to gain unfair advantage and excess revenue in Bitcoin network, but it also disrupts the normal operation of the network. In this work, we analyze the impact of selfish behavior on the Bitcoin network through a number of performance indicators such as network connectivity, block arrival rate, node response time, and block delivery time for selfish and honest blocks, respectively. We also discuss the probability of unintentional as well as intentional forks. We have found that the impact of selfish mining on network performance is noticeable, and in extreme cases, disproportional to the number of selfish miners or their hash power compared to honest nodes. Our analysis has also found that forking probability is dominated by intentional forking resulting from selfish behavior, which has the potential to increase the ledger inconsistency time and open the door to security attacks.

The aim of this work is to evaluate the impact of node churn –nodes leaving and rejoining the network– on the Bitcoin network. We provide a comprehensive analytical model for the churning process. We use a Continuous Time Markov Chain (CTMC) to describe the behavior of a node, and then apply the results to model the changes in connectivity and the impact on network performance. We analyze the time needed to resynchronize a node upon rejoining the network and find that sleep times of the order of hours require synchronization times limited by a minute. We estimate the impact of sleep and synchronization time on overall network connectivity and block/transaction distribution time. Our results show that networks with less than 4000 nodes are sensitive to churn. This occurs due to opposing impact of decrease in network size (and diameter) due to sleep time and increase of communication load per node. However, the impact of churn on network with more than 4000 nodes is noticeable but small enough to make a large Bitcoin network fairly resilient to churn.

Ultra-dense networks (UDN) are considered as one of the key technologies for advancing the widespread deployment of 5G networks. To provide continuous and reliable connectivity to user devices and to achieve the integrity and authenticity of communication, UDN needs to address the challenges related to authentication and authorization of information. Existing authentication and key agreement (AKA) schemes are not appropriate for the ultra-dense deployment of access points due to excessive authentication and/or computation overhead. This paper proposes a novel lightweight batch authentication and key agreement (LBAKA) scheme for user-centric UDN scenarios by adopting lightweight batch authentication solution and one-to-one key agreement, and by using mutual authentication to verify the credibility of both sides of the communication. The proposed LBAKA can maintain privacy of identity information under replay, impersonation and man-in-the-middle attacks in user-centric UDNs. In terms of computation and communication overhead, our simulation results show that LBAKA outperforms other similar schemes in user-centric UDNs.

Vehicular cloud computing (VCC) is composed of multiple distributed vehicular clouds (VCs), which are formed on-the-fly by dynamically integrating underutilized vehicular resources including computing power, storage, and so on. Existing proposals for identity-as-a-service (IDaaS) are not suitable for use in VCC due to limited computing resources and storage capacity of onboard vehicle devices. In this paper, we first propose an improved ciphertext-policy attribute-based encryption (CP-ABE) scheme. Utilizing the improved CP-ABE scheme and the permissioned blockchain technology, we propose a lightweight and privacy-preserving IDaaS architecture for VCC named IDaaSoVCC. It realizes lightweight and privacy-preserving access control of vehicles’ personally identifiable information (PII) in a large distributed vehicular cloud system. Security analysis demonstrates the security features of IDaaSoVCC, most notably forward secrecy, confidentiality and identity information privacy. Meanwhile, we verify that IDaaSoVCC is feasible and practical in a large distributed VC system through extensive simulations.

TGax aims at developing the IEEE 802.11ax protocol to provide enhanced throughput and power efficiency in dense WiFi deployment environments. The new specification shifts the focus from EDCA access to point coordination access, which aims at improving network utilization and power efficiency. Furthermore, it introduces, for the first time, the concept of RU allocation and orthogonal frequency division multiple access (OFDMA) techniques. The new specification addresses the spectral inefficiency in existing transmission by including schedule-based multi-user multiple-input, multiple- output (MU-MIMO) and multi-user OFDMA (MU-OFDMA) techniques to facilitate multi-user uplink transmission for high efficiency (HE) devices. However, complete adoption of the HE protocol is likely to take place over a period of time to offset the deployment cost. To ensure the coexistence of non-HE devices during the transition period, EDCA based single user transmission will remain the basic access technique and a focal point of spectral inefficiency for non-HE devices in the uplink direction. Motivated by our work on the performance of IEE 802.11ac, we propose an EDCA based uplink transmission technique for non-HE devices that allows multi-user transmission to improve spectral efficiency in coexistent WLAN networks.

As modern vehicles and distributed fog services advance apace, vehicular fog services (VFSs) are being expected to span across multiple geo-distributed datacenters, which inevitably leads to cross-datacenter authentication. Traditional cross-datacenter authentication models are not suitable for the scenario of high-speed moving vehicles accessing VFS, because these models either ignored user privacy or ignored the delay requirement of driving vehicles. This paper proposes a blockchain-assisted lightweight anonymous authentication (BLA) mechanism for distributed VFS, which is provisioned to driving vehicles. BLA can achieve the following advantages: 1) realizing a flexible cross-datacenter authentication, in which a vehicle can decide whether to be reauthenticated or not when it enters a new vehicular fog datacenter; 2) achieving anonymity, and granting vehicle users the responsibility of preserving their privacy; 3) it is lightweight by achieving noninteractivity between vehicles and service managers (SMs), and eliminating the communication between SMs in the authentication process, which significantly reduces the communication delay; and 4) resisting the attack that the database governed by one center is tampered with. BLA achieves these advantages by effectively combining modern cryptographical technology and blockchain technology. These security features are demonstrated by carrying out security analysis. Meanwhile, extensive simulations are conducted to validate the efficiency and practicality of BLA.

This paper presents a constrained application protocol enabled multithreaded Internet of Things (IoT) gateway that allows reliable and quick access to big data from IoT domains. We consider a proxy caching policy to accelerate the content delivery rate of sensor data while reducing the energy consumption of corresponding nodes. We propose a selective caching policy in which resource validation requests are sent based on user requests, besides updates from IoT nodes. We also describe an analytical model to evaluate the performance of multithreaded IoT gateway under various traffic conditions to meet the quality of service constraints. The proposed model is evaluated over single hop IPv6 over low-power wireless personal area networks/IEEE 802.15.4 cluster. The performance evaluation clearly shows the effectiveness of our model in providing fast response and reduced energy consumption of the resources.

Mobile devices may offload their applications to a virtual machine running on a cloud host. This application may fork new tasks which require virtual machines of their own on the same physical machine. Achieving satisfactory performance level in such a scenario requires flexible resource allocation mechanisms in the cloud data center. In this paper we present two such mechanisms which use prioritization: one in which forked tasks are given full priority over newly arrived tasks, and another in which a threshold is established to control the priority so that full priority is given to the forked tasks if their number exceeds a predefined threshold. We analyze the performance of both mechanisms using a Markovian multiserver queueing system with two priority levels to model the resource allocation process, and a multi-dimensional Markov system based on a Birth-Death queueing system with finite population, to model virtual machine provisioning. Our performance results indicate that the threshold-based priority scheme not only performs better, but can also be tuned to achieve the desired performance level.

The IEEE 802.11ac amendment enhances WLAN throughput by exploiting the spatial diversity of the antennas in a multi-user multi-input multi-output downlink transmission. Still, network resources remain under-utilized in uplink transmission due to single-user communication. In this paper, we propose an access point-controlled MAC protocol (A-MAC) that enables simultaneous transmissions from multiple STAs in uplink. The protocol uses the EDCA channel access technique to initiate multi-user transmission and the OFDMA method to transmit multiple RTSs simultaneously. It also introduces the explicit channel sounding technique by using dedicated OFDM subcarrier blocks for each user. Performance measurement shows that network throughput of the A-MAC is 150% higher than that of a single uplink transmission, thanks to the availability of concurrent multiple RTS transmissions in the uplink. The proposed protocol shortens the backoff time by up to 50% for all traffic categories due to concurrent multiple transmissions and thus enhances the battery life of the nodes. We observe that although the smaller backoff window of high-priority traffic category enhances the network throughput, higher intensity of high-priority traffic drives the network faster to saturation. Furthermore, better network stability and fairness among different traffic categories can be achieved when the dominant traffic has low priority.

In this paper, we consider Internet of Things (IoT) domain running multicasting constrained application protocol (CoAP) over IEEE 802.15.4 network ended by CoAP proxy/cache. We examine the features of CoAP multicasting in order to ensure freshness of data in the cache as a function of the leisure parameter which allows devices to reply in arbitrary (random) time periods after receiving multicast GET request. We also investigate communication delay in the IoT domain and daily energy consumption of devices under several leisure schemes which may be implemented at the application level or at the medium access control layer. The impact of the leisure parameter appears to be critical for congestion avoidance. We show that a combination of proactive and reactive cache update with appropriate multicast leisure scheme can achieve low probability of outdated data while limiting the energy expenditure of nodes to a satisfactory value. Furthermore, best performance with respect to delay is obtained when the leisure period is integrated in the CSMA/CA backoff process.

The constrained application protocol (CoAP) observe feature has the potential to achieve fast communication and low energy consumption. We have analyzed a CoAP proxy which stores observed data from multiple Internet of Things (IoT) nodes that belong to single IoT domain. To maintain the freshness of cached records under random data arrivals, we have implemented the evaluation of Max-Age parameter at the proxy. We have also implemented congestion control at both IoT nodes and proxy, and show that it interacts with data freshness estimation at the proxy. Our results demonstrate the capacity limits of a single IoT domain against cluster size, interobservation time, proxy re-registration rate, ratio of confirmable/nonconfirmable observations, and presence of congestion control.

The formation of cognitive personal area networks (CPANs) requires a number of nodes to connect to a dedicated coordinator node. In this paper, we propose a probabilistic blind rendezvous protocol that allows nodes to concurrently rendezvous with the CPAN coordinator. This protocol allows nodes to arrive independently and their rendezvous times to overlap partially or fully with one another. We then develop a probabilistic model of the rendezvous process for both a single node and a group of nodes. The model shows that the rendezvous time, in both cases, exhibits hyperexponential behavior with large coefficient of skewness and, consequently, large variability, which may be approximated with a Gamma distribution. Furthermore, the mean group joining time tends to flatten and may even converge to a finite limit when the number of nodes is sufficiently high. However, the variability remains high due to a long tail of the distribution.

In this paper, we examine the impact of power ramping, number of retransmission attempts, and limitations of the physical downlink control channel (PDCCH) on the performance of random access in Long Term Evolution/Long Term Evolution-Advanced networks. We demonstrate that positive feedback exists between the maximum number of retransmissions, age-based power ramping, and PDCCH deficiency. As the result, system capacity is decreased and performance deteriorates abruptly under moderate to high traffic intensity. We show that, under power ramping and default PDCCH capacity, increasing the number of retransmissions beyond 1 or at most 2 does not bring any benefits and, in fact, is detrimental to system capacity. Increasing PDCCH capacity would enable the benefits of power ramping and allow more retransmission attempts. However, increasing the number of retransmissions combined with power ramping, while helpful under moderate to high load, will decrease system capacity.

The 802.11ac version of the popular IEEE 802.11 protocol aims to boost performance by increasing the channel bandwidth and allowing downlink multiuser multiple-input multiple-output (MIMO)—i.e., simultaneous transmission to multiple clients in the downlink direction. In this paper, we evaluate the performance improvements of multiuser Transmission Opportunity (MU-TXOP) sharing in this scenario under nonsaturated load and non-ideal channel condition, using a novel analytical model based on discrete Markov chain and E-limited M/G/1 queuing model. Our results show that MU-TXOP sharing achieves up to 105% gain in throughput compared to the case without MU-TXOP sharing. Moreover, the low-priority traffic benefits most from MU-TXOP sharing, with up to 150% throughput gain at low traffic intensity due to availability of additional transmission opportunities for secondary ACs.

Long Term Evolution (LTE) and its revision, Long Term Evolution-Advanced (LTE-A), were designed for high performance communications between devices operated by human users. However, when these networks are used for Machineto- Machine (M2M) traffic, i.e., communications between smart electronic devices that do not need human supervision, performance is far from satisfactory. This is mostly due to the overload condition created when a large number of M2M devices attempts random access using the fourstep handshake prescribed by the LTE standard. In this article, we describe an overlay network that allows M2M devices to access the network without going through the full handshake. We show that the overlay provides much improved performance for M2M traffic, especially when the transmission power of M2M devices is slightly increased to combat the overload condition, and is capable of supporting M2M traffic in device-todevice (D2D) communication mode.

Radio-frequency (RF) recharging can extend maintenance-free operation of wireless sensor networks. However, the period between recharging is limited by the distance between the most distant sensor node and the master, which sends out recharging pulses. To increase this period, we propose a zoning scheme in which nodes are logically grouped into circular zones centered at the master so that nodes in a given zone send their data to their neighbors in the next closer zone, which act as relays. We describe and analyze a polling Medium Access Control (MAC) protocol that supports zoning and relaying through a probabilistic model of the energy depletion process and a queueing model of the packet transmission process. Our results indicate that zoning extends the time interval between recharge pulses and leads to equalization of node lifetimes, but limits the available data transmission bandwidth as well.

Bridging between two cognitive personal area networks (CPANs) is a fundamental step toward a reliable and efficient routing protocol. In this paper, we describe a bridging protocol for two-hop cognitive networks that use the transmission tax-based medium access control and propose ways to prioritize bridge traffic over that of ordinary nodes in both CPANs. We develop an analytical model based on probabilistic modeling and queueing theory to evaluate the performance of the bridging protocol. We validate the network performance by analyzing the waiting time of local and nonlocal packets and how the node or bridge transmission is affected by the collision with primary-source activity. Our results provide insights into the impact of various traffic and network parameters on the performance of two-hop bridging. The results indicate that the proposed protocol is effective and efficient when the bridge transfers a moderate amount of traffic.

We have developed an analytical model for a non-saturated IEEE 802.15.6 wireless body area network (WBAN) operating under an error-prone channel. The most suitable vehicle for improving network performance was found to be the choice of access phase lengths based on traffic loads for different user priorities (UPs). It was also found that the deployment of exclusive access phase (EAP) is not necessary in a typical WBAN; in fact, short exclusive and random access phases (EAP and RAP, respectively) lead to inefficient use of available bandwidth. We have also found that four user priorities (out of the eight available) typically suffice to achieve even the most stringent requirements for WBAN performance.

Wireless sensor networks that use slotted, beacon-enabled IEEE 802.15.4 standard are often implemented using clustering with randomized sleep of ordinary nodes to extend the network lifetime. To reduce the imbalance of power consumption between cluster-head and ordinary nodes, we propose a fair and efficient cluster-head rotation protocol with virtually zero overhead and no dead time. Through Markov chain modeling and probabilistic analysis, we show that the proposed approach achieves extremely good balance of nodes’ lifetime and optimal network lifetime in maximin sense, outperforming other approaches that use TDMA access.

Wireless radio frequency (RF) recharging is a promising approach to prolong the lifetime of wireless sensor networks. In this paper, we describe a polling-based MAC protocol with a round robin, one-limited service policy. Upon a specific request from one or more nodes, the coordinator sends an RF recharging pulse during which regular communication is suspended. We develop a complete probabilistic model of the energy depletion process within the proposed MAC protocol as well as a queuing model of the node behavior, and use it to investigate the behavior of the time interval between successive recharging events and the packet waiting time under varying traffic load and number of nodes in the network.

Rendezvous in cognitive networks refers to the ability of cognitive nodes to find each other and form a network, or to find and join an already operating cognitive network. Two main approached to rendezvous have emerged: sequence-based mechanism that guarantees maximum time-to-rendezvous and blind random hopping resilient to unpredictable primary user activity. In this paper we develop analytical models for time to rendezvous in the presence of primary user activity for the orthogonal sequence-based mechanism and a blind rendezvous mechanism integrated with a transmission tax-based MAC protocol with cooperative sensing. Our analysis shows that the blind mechanism performs better under random primary user activity, the difference being more pronounced when the number of channels is high and/or primary user activity is more intense. In addition, the probabilistic mechanism allows rendezvous with either an emergent or a fully operational CH-CPAN piconet without any interruption, unlike the sequence-based mechanism which precludes any data exchange during the rendezvous process.

We investigate the performance of a recovery mechanism in channel-hopping cognitive networks. The recovery algorithm is based on a list of backup channels to be used as alternatives if primary-user activity interrupts ongoing communication on the current channel; if all backup channels are exhausted without success, the nodes revert to a blind-rendezvous mechanism. We evaluate the performance of the recovery algorithm using the tools of probabilistic analysis and renewal theory and validate the results through simulation. We discuss the impact of various network and channel parameters on the performance of the recovery algorithm and show the importance of accurate sensing information for successful recovery.

Over the last few years, there has been a renewed interest in the area of mobile and ubiquitous systems, like Internet of Things and mobile cloud computing. Research advances in this area promise to transform our world with systems that will far exceed those of today in terms of: effectiveness, adaptability, autonomicity, energy efficiency, precision, reliability, safety, usability, scalability, stability and user-centric applicability. The special issue of TETC on Emerging Mobile and Ubiquitous Systems devoted to recent advances in addressing challenges on emerging systems, applications, networking, middleware, data management and services for mobile and ubiquitous computing. The Call for Papers was issued in late 2013, with the submission deadline set to 1 March 2014. We have received overwhelming submissions, and ultimately 11 high quality papers have been selected in the first part of this special issue.

In this paper, we evaluate the performance of cloud centers with high degree of virtualization and Poisson batch task arrivals. To this end, we develop an analytical model and validate it with an independent simulation model. Task service times are modeled with a general probability distribution, but the model also accounts for the deterioration of performance due to the workload at each node. The model allows for calculation of important performance indicators such as mean response time, waiting time in the queue, queue length, blocking probability, probability of immediate service, and probability distribution of the number of tasks in the system. Furthermore, we show that the performance of a cloud center may be improved if incoming requests are partitioned on the basis of the coefficient of variation of service time and batch size.

Accurate performance evaluation of cloud computing resources is a necessary prerequisite for ensuring that quality of service parameters remain within agreed limits. In this paper, we employ both the analytical and simulation modeling to addresses the complexity of cloud computing systems. Analytical model is comprised of distinct functional submodels, the results of which are combined in an iterative manner to obtain the solution with required accuracy. Our models incorporate the important features of cloud centers such as batch arrival of user requests, resource virtualization, and realistic servicing steps, to obtain important performance metrics such as task blocking probability and total waiting time incurred on user requests. Also, our results reveal important insights for capacity planning to control delay of servicing users requests.

This paper provides a systematic meta-analysis of 27 studies that investigate the impact of Test-Driven Development (TDD) on external code quality and productivity. The results indicate that, in general, TDD has a small positive effect on quality but little to no discernible effect on productivity. However, subgroup analysis has found both the quality improvement and the productivity drop to be much larger in industrial studies in comparison with academic studies. A larger drop of productivity was found in studies where the difference in test effort between the TDD and the control group's process was significant. A larger improvement in quality was also found in the academic studies when the difference in test effort is substantial; however, no conclusion could be derived regarding the industrial studies due to the lack of data. Finally, the influence of developer experience and task size as moderator variables was investigated, and a statistically significant positive correlation was found between task size and the magnitude of the improvement in quality.

The articles in this special section focus on the topic of cloud computing, technologies, applications, and new areas of technological innovation.

In this paper, we propose an analytical performance model that addresses the complexity of cloud centers through distinct stochastic submodels, the results of which are integrated to obtain the overall solution. Our model incorporates the important aspects of cloud centers such as pool management, compound requests (i.e., a set of requests submitted by one user simultaneously), resource virtualization and realistic servicing steps. In this manner, we obtain not only a detailed assessment of cloud center performance, but also clear insights into equilibrium arrangement and capacity planning that allows servicing delays, task rejection probability, and power consumption to be kept under control.

Successful development of cloud computing paradigm necessitates accurate performance evaluation of cloud data centers. As exact modeling of cloud centers is not feasible due to the nature of cloud centers and diversity of user requests, we describe a novel approximate analytical model for performance evaluation of cloud server farms and solve it to obtain accurate estimation of the complete probability distribution of the request response time and other important performance indicators. The model allows cloud operators to determine the relationship between the number of servers and input buffer size, on one side, and the performance indicators such as mean number of tasks in the system, blocking probability, and probability that a task will obtain immediate service, on the other.

In this paper, we investigate the impact of transmission opportunity (TXOP), arbitration interframe space (AIFS), and contention window on the performance of an IEEE 802.11e cluster with four traffic classes under Poisson frame arrivals. We derive an analytical model of the cluster using queuing model of individual nodes, discrete time Markov chain, and probabilistic modeling of the backoff process. The analytical model demonstrates the complex interaction between TXOP, on one side, and AIFS and contention window, on the other. We derive saturation and stability points for all traffic classes and discuss their dependency on TXOP allocations. Our results indicate that use of nonzero TXOP parameter under Poisson frame arrivals improves performance slightly by separating points of saturation and instability. More substantial performance improvements should be expected by deploying TXOP differentiation under bursty traffic. Since all traffic classes need to operate in stable, nonsaturated regime, this work has important implications for the design of congestion control and admission control schemes in IEEE 802.11e clusters.

In this paper, we investigate the performance of networks built from single-channel devices that use wireless access in vehicular environment protocols. We consider several traffic combinations, each of which presents a mix of traffic classes, over control and service channels. Our results show that time switching between the channels causes synchronization of backoff processes, which increases the frame collision probability, in particular for small sizes of contention windows. We also evaluate the impact of the interruption of the backoff process by inactive channel time, which gives rise to a probability distribution with repeated tails and a coefficient of variation larger than 1. Our model can also be used to evaluate different sets of enhanced distributed channel access parameters and to select the channel duty cycle according to the policy of the network operator.

Advances in computer and communication technology have enabled online healthcare monitoring using miniature sensing devices attached to a patient's body. Data collected in this manner is then delivered in real time, through one or more wireless hops, to the hospital network. In this article we discuss some design alternatives for the wireless portion of an integrated healthcare monitoring system, in particular issues related to its topology, the choice of wireless communication technology for tiers with well defined function, and the bridging between tiers. We also present some performance results for a two-tier topology with isolation of high-data-rate traffic from low-data-rate traffic, in which the patient's body area network is implemented using 802.15.4 low-data-rate WPAN technology, while connection in the next higher tier (i.e., from the body area network to a hospital ward network or home network) uses the ubiquitous 802.11 WLAN technology.

In this paper we consider the interconnection of an IEEE 802.15.4 body area network (BAN) in which nodes sense physiological variables such as electrocardiography (EKG), electroencephalography (EEG), pulse oximeter data, blood pressure and cardiac output, with an IEEE 802.11b room/ward WLAN. We model the operation of this two-tier network assuming that 802.15.4 BAN operates in CSMA-CA mode and that the BAN coordinator acts as the bridge which conveys BAN packets to the 802.11b access point. We analyze the two-hop network delay and discuss the mutual interaction of different data streams as well as impact of the number of bridges on packet delay.

Efficient operation of cognitive personal area networks (CPANs) necessitates accurate and efficient sensing of the primary user activity. This is accomplished in a cooperative manner by a number of nodes in the CPAN; the results of sensing are combined by the CPAN coordinator to form a comprehensive and timely channel map. The error of the sensing process is affected by various factors, including the ratio of the number of sensing nodes to the number of channels. In this paper, we present a probabilistic model of the sensing process and derive an analytical solution for the minimum number of sensing nodes that keeps the sensing error below prescribed limits. Then, we discuss three differential sensing policies in which separate sets of sensing nodes target idle and active channels only and show that the policy in which idle channels are given priority, but not exclusive treatment, achieves the best performance, as measured by the number of channels for which the information in the channel map is erroneous and the mean duration of that erroneous information.

The 802.15.4 communication standard is being increasingly used in wireless sensor networks, thanks to industry-wide support it has enjoyed in recent years. However, its support for bridging and building multicluster networks is still minimal, and a number of open issues preclude its even wider usage in practice. In this article we highlight the most important aspects of building multicluster 802.15.4 networks, review some of the design alternatives for implementing the bridging function, and discuss their pertinent properties in terms of ease of scheduling, performance, scalability, and security.

Different ways to simultaneously control event sensing reliability and device utilization in a beacon-enabled IEEE 802.15.4 sensor cluster are investigated. Activity management is achieved through probabilistic control of the duration of active and inactive (i.e., service and sleep) periods. The probabilistic control parameters are derived as functions of the required event sensing reliability, device utilization, medium access control and physical layer parameters, and network size

The performance of an IEEE 802.15.4 compliant network operating in the beacon enabled mode with both downlink and uplink traffic is analyzed through discrete time Markov chains and the theory of M/G/1 queues. The model considers acknowledged transmissions and includes the impact of different network and traffic parameters such as the packet arrival rate, packet size, inactive period between the beacons, and the number of stations. We investigate the nonsaturation region and outline the conditions under which the network abruptly goes to saturation. The analysis of stability of the network queues shows that the stability of the downlink queue at the coordinator is the most critical for network operation. Due to the abruptness with which the switch from nonsaturation to saturation occurs, the network operating point has to be carefully chosen according to the volume of downlink traffic. Furthermore, our model shows that certain features prescribed by the standard actually limit the performance of 802.15.4 networks.

Sensor networks operate under conflicting requirements of maintaining the desired value of information throughput while simultaneously maximizing the lifetime of individual nodes. In doing so, the characteristics of the operating environment, including the MAC and PHY layers, have to be taken into account as well. In this article we consider activity management of sensor nodes in a beacon-enabled IEEE 802.15.4-compliant network. Activity management is performed through lightweight probabilistic control of the duration of sleep and service periods of individual sensors. We present two distributed activity management policies which are capable of achieving and maintaining the desired network reliability while maximizing the lifetime of the entire network.

We analyze the performance of E-limited scheduling in Bluetooth piconets under bursty traffic for infinite and finite buffer cases. We show that E-limited service outperforms exhaustive service in terms of end-to-end delay and that the delays may be minimized through the proper choice of a single variable parameter: the number of packets to be exchanged during a single visit to a slave. Using analytical results for access delay and end-to-end delay, as well as suitable approximations thereof, we propose three novel admission algorithms. The simplest algorithm is based on queue stability, which makes it suitable for battery power-limited masters. The second algorithm estimates the access delay of the slave upon admission using the estimated first and second moments of the vacation times and makes the admission decision on the basis of predefined access delay bounds. The third algorithm is based on the predefined cycle time bounds, which makes it suitable for applications that generate constant-bit-rate data flows. We also present results for the performance of TCP traffic under E-limited scheduling.