Naveed Ul Hassan

Design of a Scalable Hybrid MAC Protocol for Heterogeneous M2M Networks

A robust and resilient medium access control (MAC) protocol is crucial for numerous machine-type devices to concurrently access the channel in a machine-to-machine (M2M) network. Simplex (reservation- or contention-based) MAC protocols are studied in most literatures which may not be able to provide a scalable solution for M2M networks with large number of heterogeneous devices. In this paper, a scalable hybrid MAC protocol, which consists of a contention period and a transmission period, is designed for heterogeneous M2M networks. In this protocol, different devices with preset priorities (hierarchical contending probabilities) first contend the transmission opportunities following the convention-based p-persistent carrier sense multiple access (CSMA) mechanism. Only the successful devices will be assigned a time slot for transmission following the reservation-based time-division multiple access (TDMA) mechanism. If the devices failed in contention at previous frame, to ensure the fairness among all devices, their contending priorities will be raised by increasing their contending probabilities at the next frame. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the channel utility by finding the key design parameters: the contention duration, initial contending probability, and the incremental indicator. Analytical and simulation results demonstrate the effectiveness of the proposed hybrid MAC protocol.

A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks

Wireless sensor networks (WSNs) use the unlicensed industrial, scientific, and medical (ISM) band for transmissions. However, with the increasing usage and demand of these networks, the currently available ISM band does not suffice for their transmissions. This spectrum insufficiency problem has been overcome by incorporating the opportunistic spectrum access capability of cognitive radio (CR) into the existing WSN, thus giving birth to CR sensor networks (CRSNs). The sensor nodes in CRSNs depend on power sources that have limited power supply capabilities. Therefore, advanced and intelligent radio resource allocation schemes are very essential to perform dynamic and efficient spectrum allocation among sensor nodes and to optimize the energy consumption of each individual node in the network. Radio resource allocation schemes aim to ensure QoS guarantee, maximize the network lifetime, reduce the internode and internetwork interferences, etc. In this paper, we present a survey of the recent advances in radio resource allocation in CRSNs. Radio resource allocation schemes in CRSNs are classified into three major categories, i.e., centralized, cluster-based, and distributed. The schemes are further divided into several classes on the basis of performance optimization criteria that include energy efficiency, throughput maximization, QoS assurance, interference avoidance, fairness and priority consideration, and hand-off reduction. An insight into the related issues and challenges is provided, and future research directions are clearly identified.

Energy Efficiency Tradeoff Mechanism Towards Wireless Green Communication: A Survey

Energy efficient (EE) communication has earned tremendous interest in recent years due to ever increasing number of wireless devices operating in shrinking cells, while demanding high data rates with high Quality of Services (QoS) and Quality of Expectation (QoE). To support these objectives, energy is consumed in every protocol layer. Establishing and maintaining a successful wireless communication link to simultaneously achieve all these objectives becomes challenging since the energy consumption requirements of the user and network are different for different objectives. Thus, there is a need for tradeoff techniques to achieve energy efficiency in each protocol layer. In this paper, we provide a survey of different tradeoff mechanisms proposed in the literature. The EE tradeoffs have been classified based on each protocol layer and discussed its affect in the network energy efficiency. These other QoS parameters include spectral efficiency, deployment, delay, routing, scheduling, bandwidth and coding etc. This survey also discusses the various EE techniques to improve energy-efficiency in infrastructure mode. Finally, the work provides an discussion, where impact of EE tradeoffs have been presented based on different wireless architecture towards realizing a green wireless communication network.

Electricity Cost Minimization for a Microgrid With Distributed Energy Resource Under Different Information Availability

In this paper, the electricity cost minimization problem is considered for a residential microgrid which consists of multiple households (users) equipped with renewable-based distributed energy resource (DER). Each user has a set of nonshiftable and shiftable loads. Bidirectional electricity transactions are allowed, and a dynamic pricing model for the purchasing/selling of electricity from/to the grid is proposed. In order to reduce the electricity cost, the following decisions needed to be made: 1) scheduling decisions for the shiftable loads; 2) purchasing/selling decisions for each user at each time slot; and 3) amount decisions of the electricity purchased/sold by the users. An optimization problem to minimize the total electricity cost is formulated to obtain the optimal amount of electricity consumed, sold, and purchased for each user, respectively. A centralized algorithm based on dynamic programming, Q-learning, and Lyapunov methods are proposed to solve the optimization problem with perfect information, with partial information, and without information of any time-varying parameters, respectively. For the latter two cases, distributed algorithms are designed for practical implementation. Simulation results show that the proposed schemes can provide effective management for household electricity usage and bidirectional transactions.

Optimal Fractional Frequency Reuse (FFR) and resource allocation in multiuser OFDMA system

In this paper we determine the optimal Fractional Frequency Reuse (FFR) and resource allocation in OFDMA system. Since the users at the cell edge are more exposed to inter-cell interference therefore each cell is partitioned into two regions; inner region and outer region. We determine the optimal FFR factor for the outer region, bandwidth assigned to each region and subcarrier and power allocation to all the users in the cell. The problem is formulated as sum-power minimization problem subject to minimum rate constraints in both the regions. This is a mixed linear integer programing problem which is relaxed into a convex optimization problem. We develop an efficient algorithm by using Lagrange dual decomposition theory at reasonable computational cost.

Low complexity margin adaptive resource allocation in downlink MIMO-OFDMA system

We study the downlink multiuser Multiple Input Multiple Output-Orthogonal Frequency Division Multiple Access (MIMO-OFDMA) system for margin adaptive resource allocation where the base station (BS) has to satisfy individual quality of service (QoS) constraints of the users subject to transmit power minimization. Low complexity solutions involve beamforming techniques for multiuser inter-stream interference cancellation. However, when beamforming is introduced in the margin adaptive objective, it becomes a joint beamforming and resource allocation problem. We propose a sub-optimal two-step solution which decouples beamforming from subcarrier and power allocation. First a reduced number of user groups are formed and then the problem is formulated as a convex optimization problem. Finally an efficient algorithm is developed which allocates the best user group to each subcarrier. Simulation results reveal comparable performance with the hugely complex optimal solution.

Indoor Positioning Using Visible LED Lights: A Survey

Visible light LEDs, due to their numerous advantages, are expected to become the dominant indoor lighting technology. These lights can also be switched ON/OFF at high frequency, enabling their additional use for wireless communication and indoor positioning. In this article, visible LED light--based indoor positioning systems are surveyed and classified into two broad categories based on the receiver structure. The basic principle and architecture of each design category, along with various position computation algorithms, are discussed and compared. Finally, several new research, implementation, commercialization, and standardization challenges are identified and highlighted for this relatively novel and interesting indoor localization technology.

Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids

In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customers load settings by the grid operator for peak load reduction. We suggest two different types of plans, including constant deviation plans (CDPs) and proportional deviation plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to limit the output temperature of each thermostat load and to control the number of devices eligible to participate in demand response program. We model thermostat loads as power throttling devices and design algorithms to evaluate the impact of power throttling states and plan parameters on peak load reduction. Based on the simulation results, we recommend PDPs to the customers of a residential community with variable thermostat set point preferences, while CDPs are suitable for customers with similar thermostat set point preferences. If thermostat loads have multiple power throttling states, customer engagement plans with less temperature deviations from thermostat set points are recommended. Contrary to classical ON/OFF control, higher temperature deviations are required to achieve similar amount of peak load reduction. Several other interesting tradeoffs and useful guidelines for designing mutually beneficial incentives for both the grid operator and customers can also be identified.

Electricity cost minimization for a residential smart Grid with distributed generation and bidirectional power transactions

In this paper, we consider the electricity cost minimization problem in a residential network where each community is equipped with a distributed power generation source and every household in the community has a set of essential and shiftable power demands. We allow bi-directional power transactions and assume a two-tier pricing model for the buying and selling of electricity from the grid. In this situation, in order to reduce the cost of electricity we are required to make, 1) Scheduling decisions for the shiftable demands, 2) The decisions on the amount of energy purchased from the gird by the users, 3) The decisions on the amount of energy sold to the grid by the users. We formulate a global centralized optimization problem and obtain the optimal amount of electricity consumed, sold and purchased for each household, respectively by assuming the availability of all current and future values of time-varying parameters. In reality, the lack of perfect information hampers the implementation of such global centralized optimization. Hence, we propose a distributed online algorithm which only requires the current values of the time-varying supply and demand processes. We then compare and determine the tradeoff between both formulations. Simulation results show that the proposed schemes can provide effective management for household electricity usage.

Power Control for Sum-Rate Maximization on Interference Channels Under Sum Power Constraint

In this paper, we consider the problem of power control for sum-rate maximization on multiple interfering links [transmitter-receiver pairs (Tx-RX)] under a sum power constraint. We consider a single-frequency network, where all pairs are operating in the same frequency band, thereby creating interference with each other. We study the power-allocation problem for sum-rate maximization with and without quality-of-service (QoS) requirements on individual links. When the objective is only sum-rate maximization without QoS guarantees, we develop an analytic solution to decide optimal power allocation for a two-TX-RX-pair problem. We also develop a low-complexity iterative algorithm for a three-TX-RX-pair problem. For a generic N > 3 TX-RX pair problem, we develop two low-complexity suboptimal power-allocation algorithms. The first algorithm is based on the idea of making clusters of two or three TX-RX pairs and then leveraging the power-allocation results obtained for the twoand three-TX-RX-pair problems. The second algorithm is developed by using a high signal-to-interference-plus-noise ratio (SINR) approximation, and this algorithm can be also implemented in a distributed manner by individual TXs. We then consider the same problem but with additional QoS guarantees for individual links. We again develop an analytic solution for the two-TX-RX-pair problem and a distributed algorithm for N > 2 TX-RX pairs.