Waleed Ejaz

Saliency-directed prioritization of visual data in wireless surveillance networks

Abstract In wireless visual sensor networks (WVSNs), streaming all imaging data is impractical due to resource constraints. Moreover, the sheer volume of surveillance videos inhibits the ability of analysts to extract actionable intelligence. In this work, an energy-efficient image prioritization framework is presented to cope with the fragility of traditional WVSNs. The proposed framework selects semantically relevant information before it is transmitted to a sink node. This is based on salient motion detection, which works on the principle of human cognitive processes. Each camera node estimates the background by a bootstrapping procedure, thus increasing the efficiency of salient motion detection. Based on the salient motion, each sensor node is classified as being high or low priority. This classification is dynamic, such that camera nodes toggle between high-priority and low-priority status depending on the coverage of the region of interest. High-priority camera nodes are allowed to access reliable radio channels to ensure the timely and reliable transmission of data. We compare the performance of this framework with other state-of-the-art methods for both single and multi-camera monitoring. The results demonstrate the usefulness of the proposed method in terms of salient event coverage and reduced computational and transmission costs, as well as in helping analysts find semantically relevant visual information.

Efficient Energy Management for the Internet of Things in Smart Cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy- efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities.

Energy and throughput efficient cooperative spectrum sensing in cognitive radio sensor networks

The sensing-throughput trade-off and the sensing-energy trade-off for cooperative spectrum sensing have been the subject of recent research. Two important, but often conflicting, design objectives for low-power devices are throughput and energy efficiency. These have not been comprehensively investigated jointly in cognitive radio networks for the design of spectrum sensing algorithms. This paper examines the energy-throughput trade-off for cooperative spectrum sensing and formulates an optimisation problem for the trade-off between energy and throughput for secondary users based on spectrum sensing efficiency. The objective is to minimise the energy consumed in spectrum sensing, reporting cooperative decisions to a central entity and data transmission while satisfying reliability constraints and providing a given throughput to secondary users. A heuristic solution is developed that determines the optimal sensing, reporting and transmission duration. Analysis and simulation results reveal the optimal value for sensing, reporting and transmission duration in order to achieve the best trade-off between energy consumption and throughput for secondary users. Copyright

I3S: Intelligent spectrum sensing scheme for cognitive radio networks

Reliable spectrum sensing is one of the most crucial aspects for the successful deployment of cognitive radio (CR) technology. For CR, it is not possible to transmit on a licensed band and sense it simultaneously, therefore sensing must be interleaved with transmission. Spectrum sensing in CR is challenged by a number of uncertainties, which degrade the sensing performance and in turn require much more time to achieve the targeted sensing efficiency. Hence, the authors propose a spectrum sensing scheme which obtains reliable results with less mean detection time. First, the scheme determines a better matched filter, or a combination of energy and cyclostationary detectors based on the power and band of interest. In the combined energy and cyclostationary detector, an energy detector with a bi-threshold is used, and the cyclostationary detector is applied only if the energy of the signal lies between two thresholds. Second, sensing is performed by the selection choice resulting from the first step. To evaluate the scheme’s performance, the results are compared with those where only an energy detector, matched filter, or cyclostationary detector are performed. The performance metrics are the probability of detection, probability of false alarm, and mean detection time.

Network Selection and Channel Allocation for Spectrum Sharing in 5G Heterogeneous Networks

The demand for spectrum resources has increased dramatically with the advent of modern wireless applications. Spectrum sharing, considered as a critical mechanism for 5G networks, is envisioned to address spectrum scarcity issue and achieve high data rate access, and guaranteed the quality of service (QoS). From the licensed networks perspective, the interference caused by all secondary users (SUs) should be minimized. From secondary networks point of view, there is a need to assign networks to SUs in such a way that overall interference is reduced, enabling the accommodation of a growing number of SUs. This paper presents a network selection and channel allocation mechanism in order to increase revenue by accommodating more SUs and catering to their preferences, while at the same time, respecting the primary network operators policies. An optimization problem is formulated in order to minimize accumulated interference incurred to licensed users and the amount that SUs have to pay for using the primary network. The aim is to provide SUs with a specific QoS at a lower price, subject to the interference constraints of each available network with idle channels. Particle swarm optimization and a modified version of the genetic algorithm are used to solve the optimization problem. Finally, this paper is supported by extensive simulation results that illustrate the effectiveness of the proposed methods in finding a near-optimal solution.

Fuzzy Logic Based Spectrum Sensing for Cognitive Radio Networks

It is inevitable for cognitive radio to find unutilized portion of the spectrum more accurately for successful deployment of dynamic spectrum sensing. To achieve highly reliable spectrum sensing, usually cooperative spectrum sensing is employed but still there is a margin to improve local sensing decisions. Cooperative spectrum sensing improves reliability of sensing at the cost of cooperation overhead among cognitive radio users, which can be reduced by improving local spectrum sensing. Several signal processing techniques for primary user detection have been proposed in literature but still there is room for researchers in this field to explore more sophisticated approaches to enhance sensing efficiency. This paper proposes a two stage local spectrum sensing approach. In first step, each cognitive radio perform existing spectrum sensing techniques i.e. energy detection, matched filter detection and cyclostationary detection. While in second stage, the output from each technique employed in step 1 is combined using fuzzy logic to ultimately decide about the presence or absence of primary user. The proposed approach shows significant improvement in sensing accuracy by exhibiting higher probability of detection and low false alarms.

Cooperative Wireless Energy Harvesting and Spectrum Sharing in 5G Networks

In this paper, we propose a novel best cooperative mechanism (BCM) for wireless energy harvesting and spectrum sharing in 5G networks. Data transfer and energy harvesting are finished in the designed timeslot mode. In the proposed BCM, secondary users (SUs) harvest energy from both ambient signals and primary users (PUs) signals. In addition, the SUs can act as relay for PUs and harvest energy from PU signals simultaneously. The proposed mechanism allows optimal time duration for data transfer within the timeslot. We formulate an optimization problem based on the proposed BCM with an objective to maximize throughput of PUs and SUs with constraints on data rate and energy harvest save ratios. The effectiveness of the proposed cooperative mechanism is verified by simulations, and it is considered as an important stepping stone for future research in this domain.

Distributed cooperative spectrum sensing in cognitive radio for ad hoc networks

Cognitive radio refers to an intelligent radio with the capability of sensing the radio environment and dynamically reconfiguring the operating parameters. Recent research has focused on using cognitive radios in ad hoc environments. Spectrum sensing is the most important aspect of successful cognitive radio ad hoc network deployment to overcome spectrum scarcity. Multiple cognitive radio users can cooperate to sense the primary user and improve sensing performance. Cognitive radio ad hoc networks are dynamic in nature and have no central point for data fusion. In this paper, gradient-based fully distributed cooperative spectrum sensing in cognitive radio is proposed for ad hoc networks. The licensed band used for TV transmission is considered the primary user. The gradient field changes with the energy sensed by cognitive radios, and the gradient is calculated based on the components, which include energy sensed by secondary users and received from neighbors. The proposed scheme was evaluated from the perspective of reliable sensing, convergence time, and energy consumption. Simulation results demonstrated the effectiveness of the proposed scheme.

Meat and fish freshness inspection system based on odor sensing.

We propose a method for building a simple electronic nose based on commercially available sensors used to sniff in the market and identify spoiled/contaminated meat stocked for sale in butcher shops. Using a metal oxide semiconductor-based electronic nose, we measured the smell signature from two of the most common meat foods (beef and fish) stored at room temperature. Food samples were divided into two groups: fresh beef with decayed fish and fresh fish with decayed beef. The prime objective was to identify the decayed item using the developed electronic nose. Additionally, we tested the electronic nose using three pattern classification algorithms (artificial neural network, support vector machine and k-nearest neighbor), and compared them based on accuracy, sensitivity, and specificity. The results demonstrate that the k-nearest neighbor algorithm has the highest accuracy.

Knapsack-based energy-efficient node selection scheme for cooperative spectrum sensing in cognitive radio sensor networks

A cognitive radio (CR) is the most promising candidate for the successful deployment of dynamic spectrum access (DSA). To embed DSA in a wireless sensor network, a CR is required to be installed on each sensor node. Such a sensor network is known as a cognitive radio sensor network (CRSN). Spectrum sensing is a prerequisite for a CR. Therefore every node in the CRSN consumes energy for spectrum sensing. To achieve a high-sensing accuracy, the nodes share sensing results among themselves, which is known as cooperative spectrum sensing (CSS). CSS improves sensing; however it increases energy consumption and shortens the lifetime of the network. As a CRSN is characterised as an energy constraint network, to prolong the lifetime of the network, the number of cooperating nodes should be minimum. This study presents a user selection scheme to minimise the overhead energy consumed by CSS in a CRSN. On the basis of the binary knapsack problem and its dynamic programming solution, the proposed technique selects the best nodes among the potential nodes subject to the energy constraint of the CRSN. The simulation results indicate the advantages of employing the proposed method, depending on the desired performance-energy consumption tradeoff.