This brief presents the area-time efficient decoding algorithm and architecture for the double error correcting$(63,51)$Bose–Chaudhuri–Hocquenghem (BCH) code with applications to wireless body area networks (WBANs). The formula for finding the roots of an error locator polynomial (ELP) is rederived to reduce the decoding complexity. The trace constraint for this formula is also taken into consideration to avoid the performance loss of bit error rate (BER). Hardware implementation results reveal that the proposed architecture surpasses the well-known Chien search-based and searchless decoders by the improvements of at least 50.89% and 29.35%, respectively, in terms of area-time complexity.

Bayesian inference is one method of statistical inference in machine learning. It predicts the probability that a given test belongs to a certain class and is widely used in various applications such as medical diagnosis, spam classification and fraud detection. The conventional binary architecture of computing the posterior probability is inefficient in practical implementation, which is involved in multiplication, addition and division operations. Recently, it has been shown that simple Muller C-elements, the asynchronous logic units, can perform stochastic Bayesian inference motivated by its truth table when the data is encoded as the bit-stream. The Bayesian inference machine is therefore implemented with low hardware cost. However, such an architecture is employed to compute the posterior probability of two classes only. This brief presents two stochastic circuit designs for computing the weighted ratio with multiple weights for generalized multi-class Bayesian machines. The first design is mainly based on the JK flip flop and multiplexers. The second approach is to construct the finite state machine (FSM) by manipulating the correlation between the input bit-streams. The FSM-based design requires fewer random number sources (RNSs) as compared to the JK flip flop-based implementation. These facts lead to a reduction of hardware area and energy. Simulation results show that the accuracy of the proposed JK flip flop-based and FSM-based designs is almost the same in the tested data sets. As compared to the traditional binary design, the circuit area of the proposed stochastic design is improved by$96\%$at least in the cases of three and four classes. The consumed energy per operation is reduced by$58.1\%$at least in the cases of three and four classes.

Network function virtualization (NFV), a novel network architecture, promises to offer a lot of convenience in network design, deployment, and management. This paradigm, although flexible, suffers from many risks engendering interruption of services, such as node and link failures. Thus, resiliency is one of the requirements in NFV-enabled network design for recovering network services once occurring failures. Therefore, in addition to a primary chain of virtual network functions (VNFs) for a service, one typically allocates the corresponding backup VNFs to satisfy the resiliency requirement. Nevertheless, this approach consumes network resources that can be inherently employed to deploy more services. Moreover, one can hardly recover all interrupted services due to the limitation of network backup resources. In this context, the importance of the services is one of the factors employed to judge the recovery priority. In this paper, we first assign each service a weight expressing its importance, then seek to retrieve interrupted services such that the total weight of the recovered services is maximum. Hence, we also call this issue the VNF restoration for recovering weighted services (VRRWS) problem. We next demonstrate the difficulty of the VRRWS problem is NP-hard and propose an effective technique, termed online recovery algorithm (ORA), to address the problem without necessitating the backup resources. Eventually, we conduct extensive simulations to evaluate the performance of the proposed algorithm as well as the factors affecting the recovery. The experiment shows that the available VNFs should be migrated to appropriate nodes during the recovery process to achieve better results.

There will be a fast-paced shift from conventional network systems to novel quantum networks that are supported by the quantum entanglement and teleportation, key technologies of the quantum era, to enable secured data transmissions in the next-generation of the Internet. Despite this prospect, migration to quantum networks cannot be done at once, especially on the aspect of quantum routing. In this paper, we study the maximizing entangled routing rate (MERR) problem. In particular, given a set of demands, we try to determine entangled routing paths for the maximum number of demands in the quantum network while meeting the network’s fidelity. We first formulate the MERR problem using an integer linear programming (ILP) model to capture the traffic patent for all demands in the network. We then leverage the theory of relaxation of ILP to devise two efficient algorithms including HBRA and RRA with provable approximation ratios for the objective function. To deal with the challenge of the combinatorial optimization problem in big scale networks, we also propose the path-length-based approach (PLBA) to solve the MERR problem. Using both simulations and an open quantum network simulator platform to conduct experiments with real-world topologies and traffic matrices, we evaluate the performance of our algorithms and show up the success of maximizing entangled routing rate.

This paper addresses polynomial computation using unipolar stochastic logic by exploiting correlation between the bit-streams. The AND-OR, double-NAND, OR-AND and double-NOR circuits are presented for polynomials with all positive coefficients whose sum is less than or equal to one by mathematically analyzing the joint probability distribution of coefficient bit-streams. The NAND-AND expansion is also developed for polynomials with alternatively positive and negative coefficients whose absolute values are decreasing by applying the same idea. Unlike the original methods with multiple uncorrelated random number sources (RNSs) for coefficient bit-stream generation, the presented methods only require a single RNS. Since the RNSs take up huge hardware resource in stochastic circuits, the proposed RNS-sharing techniques for polynomial computation result in a significant reduction of hardware complexity. For the factorization technique in the general polynomials, this paper enhances the original stochastic designs for the second-order polynomial and further presents the simple correlation-dependent circuits. Results show that the proposed architectures are superior to the previous ones by reducing the total number of RNSs.

In wireless sensor networks (WSNs), especially in underwater sensor networks, the problem of reporting data to the sink with minimum latency has been widely discussed in many research works. Many studies address using data aggregation to report the same type of data to the sink without data collision in a short period of time. However, due to the rapid development of sensor technology in recent years, a sensor is allowed to have multiple sensing capabilities, that is, it can generate and collect different types of data. Because different types of data have different meanings and required aggregation functions, only the data that belong to the same type are allowed to be aggregated. In addition, due to the interference of the environment or noise, the links in the WSNs are often not bidirectional. This motivates us to study the problem of using minimum latency scheduling to aggregate and report data to the sink without data collision in multiple-data-type WSNs having unidirectional links, which is shown to be NP-hard in the article. The Relative-Collision-Graph-Based Scheduling Algorithm (RCGBSA) is proposed accordingly. Simulations are conducted to demonstrate the performance of the RCGBSA.

In wireless sensor networks (WSNs), the problem of reporting data to the sink with minimum latency has been widely discussed in many research works. Many studies address on using data aggregation to report the same type of data to the sink without data collision in a short period of time. However, due to the rapid development of sensor technology in recent years, a sensor is allowed to have multiple sensing capabilities, that is, it can generate and collect different types of data. Because different types of data have different meanings and required aggregation functions, only the data that belong to the same type are allowed to be aggregated. In addition, due to the interference of the environment or the noise, the links in the WSNs are often not bidirectional. This motivates us to study the problem of using minimum latency scheduling to aggregate and report data to the sink without data collision in multiple-data-type WSNs having unidirectional links, which is shown to be NP-hard in the paper. In addition, the Relative-Collision-Graph-Based Scheduling Algorithm (RCGBSA) is therefore proposed accordingly. Simulations are conducted to demonstrate the performance of the RCGBSA.

Most of today's infrastructure systems can be efficiently operated thanks to the intelligent power supply of the smart grids. However, smart grids are highly vulnerable to malicious attacks, that is, because of the interplay between the components in the smart grids, the failure of some critical components may result in the cascading failure and breakdown of the whole system. Therefore, the question of how to identify the most critical components to protect the smart grid system is the first challenge to operators. To enable the system's robustness, there has been a lot of effort aimed at the system analysis, designing new architectures, and proposing new algorithms. However, these works mainly introduce different ranking methods for link (transmission line) or node (station) identification and directly select most the highest degree nodes or common links as the critical ones. These methods fail to address the problem of interdependencies between components nor consider the role of users that is one of critical factors impacting on the smart grid vulnerability assessment. This motivates us to study a more general and practical problem in terms of smart grid vulnerability assessment, namely the Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem. The objective of this research is to provide an efficient method to identify critical components in the system by considering a realistic attack scenario.

In wireless sensor networks (WSNs), the sensed data by sensors need to be gathered, so that one very important application is periodical data collection. There is much effort which aimed at the data collection scheduling algorithm development to minimize the latency. Most of previous works investigating the minimum latency of data collection issue have an ideal assumption that the network is a centralized system, in which the entire network is completely synchronized with full knowledge of components. In addition, most of existing works often assume that any (or no) data in the network are allowed to be aggregated into one packet and the network models are often treated as tree structures. However, in practical, WSNs are more likely to be distributed systems, since each sensor's knowledge is disjointed to each other, and a fixed number of data are allowed to be aggregated into one packet. This is a formidable motivation for us to investigate the problem of minimum latency for the data aggregation without data collision in the distributed WSNs when the sensors are considered to be assigned the channels and the data are compressed with a flexible aggregation ratio, termed the minimum-latency collision-avoidance multiple-data-aggregation scheduling with multi-channel (MLCAMDAS-MC) problem. A new distributed algorithm, termed the distributed collision-avoidance scheduling (DCAS) algorithm, is proposed to address the MLCAMDAS-MC. Finally, we provide the theoretical analyses of DCAS and conduct extensive simulations to demonstrate the performance of DCAS.

There is much effort which aimed at the data collection scheduling algorithm development to minimize the latency in wireless sensor networks (WSNs). Most of previous works investigating the minimum latency of data collection issue have an ideal assumption that the network is a centralized system, in which the entire network is completely synchronized with full knowledge of components. In addition, most of existing works often assume that any data in the network are allowed to be aggregated into one packet and the network models are often treated as tree structures. However, in practical, WSNs are more likely to be distributed systems, since each sensor's knowledge is disjointed to each other, and a fixed number of data are allowed to be aggregated into one packet. In this paper, we investigate the problem of minimum latency for the data aggregation without data collision in the distributed WSNs when the sensors are considered to be assigned the channels, termed the minimum-latency collision-avoidance multiple-data-aggregation scheduling with multi-channel (MLCAMDAS-MC) problem. A new distributed algorithm, termed the distributed collision-avoidance scheduling (DCAS) algorithm, is proposed to address the MLCAMDAS-MC. Finally, we conduct extensive simulations to demonstrate the performance of DCAS.

Geographic routing protocols route packets in a hop-by-hop manner, where a node selects a relay node to forward packets among the (1-hop) neighboring nodes based on the obtained (geographic) location information of the neighboring nodes. To employ geographic routing protocols, two neighboring nodes need to exchange the location information with each other periodically. In a mobile ad hoc network, however, a packet transmitted between two neighboring nodes may be lost due to the out-of-date location information, resulting in demanding extra energy to retransmit the packet. In this paper, by considering the out-of-date neighboring location information, we propose two methods capable of augmenting geographic routing protocols to reduce energy consumption in mobile ad hoc networks. The first one considers a tradeoff between the progress distance and the energy consumption when selecting a relay node. The second one puts emphasis only on the energy consumption when selecting a relay node, and it consumes minimum energy to route a packet between a source-destination pair in the continuous domain. Simulations show that geographic routing protocols augmented with our methods can significantly reduce the energy consumption while preserving the high packet delivery rate.

In mobile wireless sensor networks (MWSNs), each sensor has the ability not only to sense and transmit data but also to move to some specific location. Because the movement of sensors consumes much more power than that in sensing and communication, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance of mobile sensors is minimized has received a great deal of attention. However, in reality, due to a limited budget or numerous targets, mobile sensors may be not enough to cover all targets or form a connected network. Therefore, targets must be weighted by their importance. The more important a target, the higher the weight of the target. A more general problem for target coverage and network connectivity, termed the Maximum Weighted Target Coverage and Sensor Connectivity with Limited Mobile Sensors (MWTCSCLMS) problem, is studied. In this paper, an approximation algorithm, termed the weighted-maximum-coverage-based algorithm (WMCBA), is proposed for the subproblem of the MWTCSCLMS problem. Based on the WMCBA, the Steiner-tree-based algorithm (STBA) is proposed for the MWTCSCLMS problem. Simulation results demonstrate that the STBA provides better performance than the other methods.

Abstract In many applications, sensor nodes are deployed in a 3D environment with obstacles, in which case a great deal of holes exist in 3D wireless sensor networks constructed. Recently, several geographic routing protocols are proposed for 3D wireless sensor networks. Each of them, however, cannot guarantee packet delivery or demands a long routing path to turn around a hole. In this paper, we first introduce a method of constructing a guide to the navigation on the surface of a hole. Subsequently, a geographic routing protocol termed the Greedy‐Guide_Navigation‐Greedy protocol (GGNG) that can always route a packet to turn around a hole with the help of the guide is proposed. GGNG guarantees packet delivery and can be extended toward a mobile sensor network in a limited 3D space. Simulations show that the path stretch of each routing protocol to GGNG in approximately 90%of the cases is between 1.02 and 189.24. In addition, the number of messages transmitted by a node surrounding a hole in the guide construction is approximately three. Copyright © 2014 John Wiley & Sons, Ltd.

Because of the wide range of applications, many geographic routing protocols have been proposed in three-dimensional 3D wireless sensor networks. However, all the methods require assistance from a global positioning system GPS, which is not always available. In this paper, we propose a method of constructing an axis-based virtual coordinate assignment in 3D wireless sensor networks ABVCap_3D that requires no GPS assistance. We also propose a routing protocol based on ABVCap_3D, which guarantees packet delivery in 3D networks. Using simulations, we evaluate the performance of ABVCap_3D routing and other well-known routing protocols, such as greedy-random-greedy routing, greedy-hull-greedy routing, and the routing based on axis-based virtual coordinate assignment in 2D wireless sensor networks ABVCap routing. Simulations show that ABVCap_3D routing requires significantly relative lower cost for guaranteeing packet delivery in comparison with ABVCap routing. Simulations also demonstrate that ABVCap_3D routing ensures a moderate ratio for routing path length to the shortest ideal path length. Copyright © 2012 John Wiley & Sons, Ltd.

We study the information brokerage schemes in wireless sensor networks, which allow consumers to obtain data from producers by replicating and retrieving data in a certain set of sensors, and propose a novel information brokerage scheme, termed RDRIB. Unlike existing information brokerage schemes, RDRIB guarantees successful data retrieval without using any boundary detection algorithm and the geographic location information acquired by the global positioning system (GPS). In RDRIB, the double-ruling technique is used to replicate and retrieve the data within a constructed virtual boundary, and simulations show that RDRIB has good performance in terms of the replication memory overhead, the replication message overhead, the retrieval message overhead, the retrieval latency, and the construction message overhead.

In the tracking system, a better prediction model can significantly reduce power consumption in a wireless sensor network because fewer redundant sensors will be activated to keep monitoring the object. The Gauss-Markov mobility model is one of the best mobility models to describe object trajectory because it can capture the correlation of object velocity in time. Traditionally, the Gauss-Markov parameters are estimated using an autocorrelation technique or a recursive least-squares estimation technique; either of these techniques, however, requires a large amount of historical movement information of the mobile object, which is not suitable for tracking objects in a wireless sensor network because they demand a considerable amount of message communication overhead between wireless sensors which are usually battery powered. In this paper, we develop a Gauss-Markov parameter estimator for wireless sensor networks (GMPE_MLH) using a maximum likelihood technique. The GMPE_MLH model estimates the Gauss-Markov parameters with few requirements in terms of message communication overhead. Simulations demonstrate that the GMPE_MLH model generates negligible differences between the actual and estimated values of the Gauss-Markov parameters and provides comparable prediction of the mobile object's location to the Gauss-Markov parameter estimators using an autocorrelation technique or a recursive least-squares estimation.

Wireless sensor networks are formed by connected sensors that each have the ability to collect, process, and store environmental information as well as communicate with others via inter-sensor wireless communication. These characteristics allow wireless sensor networks to be used in a wide range of applications. In many applications, such as environmental monitoring, battlefield surveillance, nuclear, biological, and chemical (NBC) attack detection, and so on, critical areas and common areas must be distinguished adequately, and it is more practical and efficient to monitor critical areas rather than common areas if the sensor field is large, or the available budget cannot provide enough sensors to fully cover the entire sensor field. This provides the motivation for the problem of deploying the minimum sensors on grid points to construct a connected wireless sensor network able to fully cover critical square grids, termed CRITICAL-SQUARE-GRID COVERAGE. In this paper, we propose an approximation algorithm for CRITICAL-SQUARE-GRID COVERAGE. Simulations show that the proposed algorithm provides a good solution for CRITICAL-SQUARE-GRID COVERAGE.

A wireless sensor network has unidirectional links because sensors can have different transmission ranges, sensors have unstable transmission ranges, and a hidden terminal problem exists. In this paper, we introduce a virtual coordinate assignment protocol (ABVCap_Uni) to assign virtual coordinates to nodes that have no geographic information in wireless sensor networks with unidirectional links, and we propose a routing protocol based on the ABVCap_Uni virtual coordinates. Our routing protocol guarantees packet delivery without computation and storage of global topology features in a discrete domain. Using simulation, we evaluate the performance of the proposed routing protocol (ABVCap_Uni routing), the greedy landmark- descent routing protocol (GLDR+VLM routing), and the greedy routing protocol based on physical coordinates (Euclidean routing). The simulations demonstrate that our routing protocol ensures moderate routing path length cost overhead.

Wireless sensor networks have often been used to monitor and report the locations of moving objects. Since sensors can also be used for storage, a wireless sensor network can be considered a distributed database, enabling us to update and query the location information of moving objects. Many researchers have studied the problem of how to construct message-pruning trees that can update a database and query objects with minimum cost (the Minimum Cost Message-Pruning Tree problem). The trees are constructed in such a way that the total cost of updating the database and querying objects is kept as minimum as possible, while the hardness of the Minimum Cost Message-Pruning Tree problem remains unknown. In this paper, we first show that the Minimum Cost Message-Pruning Tree problem is NP-complete. Subsequently, since the message-pruning tree with minimum cost is hard to be constructed in polynomial time, we propose a new data aggregation structure, a message-pruning tree with shortcuts, instead of the message-pruning tree. Simulation results show that the proposed data aggregation structure significantly reduces the total cost of updating the database and querying objects, as compared to the message-pruning tree.

This paper proves that deploying sensors on grid points to construct a wireless sensor network that fully covers critical grids using minimum sensors (critical-grid coverage problem) and that fully covers a maximum total weight of grids using a given number of sensors (weighted-grid coverage problem) are each NP-complete

A core-based group-shared multicast tree is a shortest path tree rooted at core node that distributes packets to and from all group members. Traditionally, the bandwidth cost consumed by transmitting a packet via the tree is evaluated by the total weights of all the edges. And, the cost is minimized by constructing the multicast tree that has minimum total weights of edges to span all group members. However, when the local broadcasting operation is used to multicast a packet, we found that the cost is supposed to be evaluated by the total weights of all senders that include the core and all non-leaves. Since the multicast tree with the number of nodes greater than or equal to three has minimum cost only when the core is not a leaf it leads us to find the multicast tree with the minimum number of non-leaves when each sender node has a unit weight. However, no polynomial time approximation scheme can be found for the minimum non-leaf multicast tree problem unless P=NP since the problem is not only NP-hard but also MAX-SNP hard. Thus, a heuristic is proposed to dynamically construct and adjust the multicast tree in a mobile ad hoc network. Experimental results show that our multicast tree has smaller number of non-leaves than others in the geometrically distributed network model.