Adnan Shahid

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.

A Decentralized Heuristic Approach towards Resource Allocation in Femtocell Networks

Femtocells represent a novel configuration for existing cellular communication, contributing towards the improvement of coverage and throughput. The dense deployment of these femtocells causes significant femto-macro and femto-femto interference, consequently deteriorating the throughput of femtocells. In this study, we compare two heuristic approaches, i.e., particle swarm optimization (PSO) and genetic algorithm (GA), for joint power assignment and resource allocation, within the context of the femtocell environment. The supposition made in this joint optimization is that the discrete power levels are available for the assignment. Furthermore, we have employed two variants of each PSO and GA: inertia weight and constriction factor model for PSO, and twopoint and uniform crossover for GA. The two proposed algorithms are in a decentralized manner, with no involvement of any centralized entity. The comparison is carried out between the two proposed algorithms for the aforementioned joint optimization problem. The contrast includes the performance metrics: including average objective function, min–max throughput of the femtocells, average throughput of the femto users, outage rate and time complexity. The results demonstrate that the decentralized PSO constriction factor outperforms the others in terms of the aforementioned performance metrics.

Joint Sensor-Node Selection and Channel Allocation Scheme for Cognitive Radio Sensor Networks

The conventional wireless sensor networks (WSNs) operating on the license-free ISM band are now experiencing large interference from other ISM band based wireless devices. The cognitive radio sensor network (CRSN) can solve this issue of interference and it can provide better services within the domain of the WSNs. In this paper, we propose a novel joint sensor node selection and channel allocation scheme to improve the performance of the CRSNs. We consider the cluster oriented sensor network and form clusters using the K-means clustering algorithm. The node selection scheme is formulated using the knapsack problem where each cluster head selects the optimal number of sensor nodes (SNs). Later on, the Hungarian algorithm is employed to allocate the best channels among SNs for their reporting process. Simulation results show that our scheme outperforms the existing schemes in terms of prolonging the network life, selecting the more reliable SNs and allocating the optimal channels among SNs for their data transmission during the reporting process.

Optimization of the Overall Success Probability of the Energy Harvesting Cognitive Wireless Sensor Networks

Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings.

Energy Harvesting Non-Orthogonal Multiple Access System With Multi-Antenna Relay and Base Station

In this paper, we consider downlink non-orthogonal multiple access cooperative communication system. The base station (BS) serves two types of users, which are named relay user (RU) and far user (FU). The BS and RU are equipped with multiple transmit antennas. The RU harvests energy from the BS transmissions to perform the relaying operation for the FU. We have considered 1) amplify-forward; 2) decode-forward; and 3) quantize-map-forward relaying protocols at the RU. As the BS and RU have multiple antennas, therefore we consider 1) beamforming and 2) random antenna selection strategies at the BS and RU. Closed form expressions for the outage probability are provided for the aforementioned relay protocols and antenna strategies. Further, we show that for certain data rate range of the relay and FU the quantize-map-forward relaying protocol can perform better than the other two relaying protocols.

Self-Organized Energy-Efficient Cross-Layer Optimization for Device to Device Communication in Heterogeneous Cellular Networks

Device to device (D2D) communication brings numerous benefits for future heterogeneous cellular networks. However, an energy-efficient design of such D2D communications is a critical challenge due to the cochannel deployment and limited power of users. In this paper, we present an energy-efficient self-organized cross-layer optimization scheme, which aims to maximize the D2D communication energy-efficiency without jeopardizing the quality of service (QoS) requirements of other tiers. Specifically, we model the cross-layer optimization, which includes resource block (RB) and power allocation using a noncooperative game. In the proposed scheme, each D2D transmitter user, which is a player in the game, operates in a self-organizing manner and selects the RBs and the power levels for enhancing its energy efficiency while maintaining the QoS requirements of other heterogeneous parties. Concerning the computationally intense nature of the global optimization problem, we decompose the problem into two subproblems: the RB allocation and the power allocation, and solve them iteratively in a game-theoretic manner. Simulation results demonstrate superior energy efficiency performance of the proposed scheme over conventional schemes. In addition, it is also shown via simulation that the performance of the proposed scheme degrades if the channel state information is not precisely available.

Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques

The Internet of Things (IoT) has gained an incredible importance in the communication and networking industry due to its innovative solutions and advantages in diverse domains. The IoT’ network is a network of smart physical objects: devices, vehicles, buildings, etc. The IoT has a number of applications ranging from smart home, smart surveillance to smart healthcare systems. Since IoT consists of various heterogeneous devices that exhibit different traffic patterns and expect different quality of service (QoS) in terms of data rate, bit error rate and the stability index of the channel, therefore, in this paper, we formulated an optimization problem to assign channels to heterogeneous IoT devices within a smart building for the provisioning of their desired QoS. To solve this problem, a novel particle swarm optimization-based algorithm is proposed. Then, exhaustive simulations are carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrate the supremacy of our proposed algorithm over the existing ones in terms of throughput, bit error rate and the stability index of the channel.

Cooperation techniques between LTE in unlicensed spectrum and Wi-Fi towards fair spectral efficiency

On the road towards 5G, a proliferation of Heterogeneous Networks (HetNets) is expected. Sensor networks are of great importance in this new wireless era, as they allow interaction with the environment. Additionally, the establishment of the Internet of Things (IoT) has incredibly increased the number of interconnected devices and consequently the already massive wirelessly transmitted traffic. The exponential growth of wireless traffic is pushing the wireless community to investigate solutions that maximally exploit the available spectrum. Recently, 3rd Generation Partnership Project (3GPP) announced standards that permit the operation of Long Term Evolution (LTE) in the unlicensed spectrum in addition to the exclusive use of the licensed spectrum owned by a mobile operator. Alternatively, leading wireless technology developers examine standalone LTE operation in the unlicensed spectrum without any involvement of a mobile operator. In this article, we present a classification of different techniques that can be applied on co-located LTE and Wi-Fi networks. Up to today, Wi-Fi is the most widely-used wireless technology in the unlicensed spectrum. A review of the current state of the art further reveals the lack of cooperation schemes among co-located networks that can lead to more optimal usage of the available spectrum. This article fills this gap in the literature by conceptually describing different classes of cooperation between LTE and Wi-Fi. For each class, we provide a detailed presentation of possible cooperation techniques that can provide spectral efficiency in a fair manner.

Reliable, Efficient, and Power Optimized Control-Channel Selection Scheme for Cognitive Radio Networks

This paper presents a centralized control-channel selection scheme for cognitive radio networks (CRNs) by exploiting the variation in the spectrum across capacity, occupancy, and error rate. We address the fundamental challenges in the design of the control-channel for CRNs: (1) random licensed users (LUs) activity and (2) the economical and vulnerability concerns for a dedicated control-channel. We develop a knapsack problem (KP) based reliable, efficient, and power optimized (REPO) control-channel selection scheme with an optimal data rate, bit error rate (BER), and idle time. Moreover, we introduce the concept of the backup channels in the context of control-channel selection, which assists the CRs to quickly move on to the next stable channel in order to cater for the sudden appearance of LUs. Based on the KP and its dynamic programming solution, simulation results show that the proposed scheme is highly adaptable and resilient to random LU activity. The REPO scheme reduces collisions with the LUs, minimizes the alternate channel selection time, and reduces the bit error rate (BER). Moreover, it reduces the power consumed during channel switching and provides a performance, that is, competitive with those schemes that are using a static control-channel for the management of control traffic in CRNs.

Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices

Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals’ modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI’s probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.