Lifetime maximization routing with network coding in wireless multihop networks

In this paper, we consider the lifetime maximization routing with network coding in wireless mul- tihop networks. We first show that lifetime maximization with network coding is different from pure routing, throughput maximization with network coding and energy minimization with network coding. Then we formulate lifetime maximization problems in three different cases of (i) no network coding, (ii) two-way network coding, and (iii) overhearing network coding. To solve these problems, we use flow augmenting routing (FA) for the first case, and then extend the FA with network coding (FANC) by using energy minimized one-hop network coding. After that, we investigate the influence of parameters of FANC, evaluate the performance of FANC with two-way and overhearing network coding schemes and compare it with that without network coding under two different power control models, namely, protocol and physical ones. The results show that the lifetime can be improved significantly by using network coding, and the performance gain of network coding decreases with the increase of flow asymmetry and the power control ability.     

Optimal energy allocation in heterogeneous wireless sensor networks for lifetime maximization

We consider the problem of optimal energy allocation and lifetime maximization in heterogeneous wireless sensor networks. We construct a probabilistic model for heterogeneous wireless sensor networks where sensors can have different sensing range, different transmission range, different energy consumption for data sensing, and different energy consumption for data transmission, and the stream of data sensed and transmitted from a sensor and the stream of data relayed by a sensor to a base station are all treated as Poisson streams. We derive the probability distribution and the expectation of the number of data transmissions during the lifetime of each sensor and the probability distribution and the expectation of the lifetime of each sensor. In all these analysis, energy consumption of data sensing and data transmission and data relay are all taken into consideration. We develop an algorithm to find an optimal initial energy allocation to the sensors such that the network lifetime in the sense of the identical expected sensor lifetime is maximized. We show how to deal with a large amount of energy budget that may cause excessive computational time by developing accurate closed form approximate expressions of sensor lifetime and network lifetime and optimal initial energy allocation. We derive the expected number of working sensors at any time. Based on such results, we can find the latest time such that the expected number of sensors that are still functioning up to that time is above certain threshold.     

Network lifetime maximization for time-sensitive data gathering in wireless sensor networks

Energy-constrained sensor networks have been widely deployed for environmental monitoring and security surveillance purposes. Since sensors are usually powered by energy-limited batteries, in order to prolong the network lifetime, most existing research focuses on constructing a load-balanced routing tree rooted at the base station for data gathering. However, this may result in a long routing path from some sensors to the base station. Motivated by the need of some mission-critical applications that require all sensed data to be received by the base station with minimal delay, this paper aims to construct a routing tree such that the network lifetime is maximized while keeping the routing path from each sensor to the base station minimized. This paper shows that finding such a tree is NP-hard. Thus a novel heuristic called top-down algorithm is presented, which constructs the routing tree layer by layer such that each layer is optimally extended, using a network flow model. A distributed refinement algorithm is then devised that dramatically improves on the load balance for the routing tree produced by the top-down algorithm. Finally, extensive simulations are conducted. The experimental results show that the top-down algorithm with balance-refinement delivers a shortest routing tree whose network lifetime achieves around 85% of the optimum.     

Dynamic end-to-end capacity in IEEE 802.16 wireless mesh networks

The IEEE 802.16 standard defines mesh mode as one of its two operational modes in medium access control (MAC). In the mesh mode, peer-to-peer communication between subscriber stations (SSs) is allowed, and transmissions can be routed via other SSs across multiple hops. In such an IEEE 802.16 mesh network, accurate and reliable determination of dynamic link capacity and end-to-end capacity of a given multi-hop route is crucial for robust network control and management. The dynamic capacities are difficult to determine in a distributed system due to decentralized packet scheduling and interference between communicating nodes caused by the broadcast nature of radio propagation. In this paper, we first propose a method for computing the dynamic link capacity between two mesh nodes, and extend that to determine the dynamic end-to-end capacity bounds of a multi-hop route based on the concept of Bottleneck Zone. The physical deployments of networks are also considered in the capacity estimation. We demonstrate the effectiveness and accuracy of our methods for computing dynamic link capacity and end-to-end capacity bounds through extensive simulations.     

On the capacity of UWB-based wireless sensor networks

Ultra-wideband (UWB) is a promising wireless communications technology and has great potential for emerging applications such as sensor networks. This paper studies the following fundamental problems for UWB-based sensor networks. For a given network instance, what is the maximum data rate (network capacity) that can be received at the base station (i.e., sink node)? What is the network capacity bound among arbitrary network instances? We show that these problems can be cast into a cross-layer formulation with joint consideration of routing, scheduling, power control, and rate assignment. For a given network instance, we find a closed-form network capacity as well as corresponding optimal routing, scheduling, power control, and rate assignment. We also find a network capacity bound among arbitrary network instances. Our results provide fundamental results for UWB-based sensor networks.     

Continuous data aggregation and capacity in probabilistic wireless sensor networks

Due to the existence of many probabilistic lossy links in Wireless Sensor Networks (WSNs) (Liu et al., 2010)  [25], it is not practical to study the network capacity issue under the Deterministic Network Model (DNM). A more realistic one is actually the Probabilistic Network Model (PNM). Therefore, we study the Snapshot Data Aggregation (SDA) problem, the Continuous Data Aggregation (CDA) problem, and their achievable capacities for probabilistic WSNs under both the independent and identically distributed (i.i.d.) node distribution model and the Poisson point distribution model in this paper. First, we partition a network into cells and use two vectors to further partition these cells into equivalent color classes. Subsequently, based on the partitioned cells and equivalent color classes, we propose a Cell-based Aggregation Scheduling (CAS) algorithm for the SDA problem in probabilistic WSNs. Theoretical analysis of CAS and the upper bound capacity of the SDA problem show that the achievable capacities of CAS are all order optimal in the worst case, the average case, and the best case. For the CDA problem in probabilistic WSNs, we propose a Level-based Aggregation Scheduling (LAS) algorithm. LAS gathers the aggregation values of continuous snapshots by forming a data aggregation/transmission pipeline on the segments and scheduling all the cell-levels in a cell-level class concurrently. By theoretical analysis of LAS and the upper bound capacity of the CDA problem, we prove that LAS also successfully achieves order optimal capacities in all the cases. The extensive simulation results further validate the effectiveness of CAS and LAS.     

Power control and capacity of spread spectrum wireless networks

Transmit power control is a central technique for resource allocation and interference management in spread-spectrum wireless networks. With the increasing popularity of spread-spectrum as a multiple access technique, there has been significant research in the area in recent years. While power control has been considered traditionally as a means to counteract the harmful effect of channel fading, the more general emerging view is that it is a flexible mechanism to provide quality of service to individual users. In this paper, we will review the main threads of ideas and results in the recent development of this area, with a bias towards issues that have been the focus of our own research. For different receivers of varying complexity, we study both questions about optimal power control as well as the problem of characterizing the resulting network capacity. Although spread-spectrum communications has been traditionally viewed as a physical-layer subject, we argue that by suitable abstraction, many control and optimization problems with interesting structure can be formulated at the network layer.     

A centralized immune-Voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks

Saving energy is a most important challenge in Mobile Wireless Sensor Networks (MWSNs) to extend the lifetime, and optimal coverage is the key to it. Therefore, this paper proposes a Centralized Immune-Voronoi deployment Algorithm (CIVA) to maximize the coverage based on both binary and probabilistic models. CIVA utilizes the multi-objective immune algorithm that uses the Voronoi diagram properties to provide a better trade-off between the coverage and the energy consumption. The CIVA algorithm consists from two phases to improve the lifetime and the coverage of MWSN. In the first phase, CIVA controls the positions and the sensing ranges of Mobile Sensor Nodes (MSNs) based on maximizing the coverage and minimizing the dissipated energy in mobility and sensing. While the second phase of CIVA adjusts the radio (sleep/active) of MSNs to minimize the number of active sensors based on minimizing the consumption energy in sensing and redundant coverage and preserving the coverage at high level. The performance of the CIVA is compared with the previous algorithms using Matlab simulation for different network configurations with and without obstacles. Simulation results show that the CIVA algorithm outperforms the previous algorithms in terms of the coverage and the dissipated energy for different networks configurations.     

Multi-stage opinion maximization in social networks

Neural Computing and Applications - Opinion maximization is a crucial optimization approach, which can be used in preventative health, such as heart disease, stroke or diabetes. The key issue of...     

Multi-period design of survivable wireless access networks under capacity constraints

Design of survivable wireless access networks plays a key role in the overall design of a wireless network. In this research, the multi-period design of a wireless access network under capacity and survivability constraints is considered. Given the location of the cells and hubs, the cost of interconnection, and the demands generated by the cells, the goal of the designer is to find the best interconnection between cells and hubs so that the overall connection cost is minimized and the capacity and the survivability constraints are met. Integer programming formulations for this problem are proposed and the problems are solved using heuristic methods. Using different combination of network sizes, demand patterns and various time periods, a number of numerical experiments are conducted and all of them are found to yield high quality solutions.     

On the nominal capacity of multi-radio multi-channel wireless mesh networks

Wireless mesh networking is an emerging technology with a wide range of applications, from community to metropolitan area networking. Mesh networks consist of wireless routers and access points that make up the network backbone which is connected to the wired infrastructure. Client devices are located at the edge. The capacity of a wireless mesh network is an important performance indicator as well as design parameter. It depends on various factors such as network size and topology, expected traffic patterns, number of interfaces per node, number of channels, routing and channel assignment etc. In this paper, we present an analytical framework for estimating the capacity of wireless mesh networks, based on the concept of collision domains. The capacity of grid-oriented mesh networks is investigated for various scenarios to study the impact of different network parameters.     

On the capacity of multi-packet reception enabled multi-channel multi-interface wireless networks

Multi-packet reception (MPR) offers increased number of concurrent transmissions, which improves the capacity of wireless Ad hoc networks. By equipping each node with multiple wireless interfaces, the transmissions can be separated into different channels and the throughput can be increased. Combining these two technologies, this work studies the capacity of wireless networks wherein each node is equipped with multiple interfaces and each interface can decode at most k concurrent transmissions within its receiving range.     

Efficient algorithms for influence maximization in social networks

In recent years, due to the surge in popularity of social-networking web sites, considerable interest has arisen regarding influence maximization in social networks. Given a social network structure, the problem of influence maximization is to determine a minimum set of nodes that could maximize the spread of influences. With a large-scale social network, the efficiency and practicability of such algorithms are critical. Although many recent studies have focused on the problem of influence maximization, these works in general are time-consuming when a social network is large-scale. In this paper, we propose two novel algorithms, CDH-Kcut and Community and Degree Heuristic on Kcut/SHRINK, to solve the influence maximization problem based on a realistic model. The algorithms utilize the community structure, which significantly decreases the number of candidates of influential nodes, to avoid information overlap. The experimental results on both synthetic and real datasets indicate that our algorithms not only significantly outperform the state-of-the-art algorithms in efficiency but also possess graceful scalability.     

Profit maximization and risk minimization in semi-Markovian networks

The problem of profit maximization and risk minimization under stationary conditions is considered for multichannel semi-Markovian networks. Systems of integral equations that describe data handling and profit accumulation in the network are subjected to asymptotic analysis. The objective functions of the optimization problems are expressed explicitly in terms of network parameters. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 65–79, March–April 2007.