We propose a distributed inter-base station (BS) cooperation assisted load balancing framework for improving energy efficiency of OFDMA-based cellular access networks. Proposed cooperation is formulated following the principle of ecological self-organization. Based on the network traffic, BSs mutually cooperate for distributing traffic among themselves and thus, the number of active BSs is dynamically adjusted for energy savings. For reducing the number of communications among BSs, a three-step measure is taken by using estimated load factor (LF), initializing the algorithm with only the active BSs and differentiating neighboring BSs according to their operating modes for distributing traffic.An exponentially weighted moving average (EWMA)-based technique is proposed for estimating the LF in advance based on the historical data. Various selection schemes for finding the best BSs to distribute traffic are also explored. Furthermore, we present an analytical formulation for modeling the dynamic switching of BSs. A thorough investigation under a wide range of network settings is carried out in the context of an LTE system. Results demonstrate a significant enhancement in network energy efficiency yielding up to 30% higher savings than the compared schemes. Moreover, frequency of correspondence among BSs can be reduced up to 80%. Index TermsEnergy efficiency, inter-BS cooperation, load balancing, radio access network.

Abstract-In this paper, we propose traffic-sensitive dynamic sectorization of base stations (BSs) for energy savings in OFDMAbased cellular access networks. Under the proposal, following the temporal variation of traffic level, BSs are dynamically reconfigured using fewer sectors. User data rate, service continuity and network coverage are also maintained. A generalized energy saving optimization problem is formulated, which is a challenging combinatorial problem. Therefore, a low complexity greedy style heuristic algorithm is presented. Effectiveness of the scheme is demonstrated using Monte Carlo simulations and compared with the popular BS on/off based energy saving technique.

Improving energy efficiency, reducing carbon footprint and self-sustainability are key concerns in the design and development of future green communication networks. Therefore, in this paper, a novel energy efficient cellular access network architecture based on the principle of ecological protocooperation is proposed. Furthermore, for the first time, the wake-up technology is introduced to cellular access networks for implementing the proposed cooperative architecture. According to our proposal, base transceiver stations (BTSs) cooperatively and dynamically make intelligent decisions for switching between different power modes depending on network traffic conditions. Next, an extensive simulation process under different traffic patterns is carried out for identifying network parameters corresponding to optimal energy savings. The analysis of results reveals that the proposed architecture is capable of substantially reducing the energy consumption. In addition, as a secondary result, the proposed architecture offers an additional level of sustainability to the cellular access network infrastructure.

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SUMMARYIn this paper, cross-optimization of accuracy, latency, and energy in wireless sensor networks (WSNs) through infection spreading is investigated. Our solution is based on a dual-layer architecture for efficient data harvesting in a WSN, in which, the lower layer sensors are equipped with a novel adaptive data propagation method inspired by infection spreading and the upper layer consists of randomly roaming data harvesting agents. The proposed infection spreading mechanisms, namely random infection (RI) and linear infection (LI), are implemented at the lower layer. The entire sensor field is dynamically separated into several busy areas (BA) and quiet areas (QA). According to the BA or QA classification, the level of importance is defined, on which, the optimal number of infections for a particular observation is evaluated. Therefore, the accessed probability for observations with a relatively higher importance level is adaptively increased. The proposed mechanisms add further value to the data harvesting operation by compensating for its potential lack of coverage due to random mobility and tolerable delay, thus a relatively higher accuracy and latency requirements can be guaranteed for the optimization of energy consumption in a dynamically changing environment. Further, with the cost of processing simple location information, LI is proved to outperform RI.

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Intrusion detection is an important and challenging problem that has a major impact on quality and reliability of smart city services. To this extent, replay attacks have been one of the most common threats on smart city infrastructure, which compromises authentication in a smart city network. For example, a replay attack may physically damage smart city infrastructure resulting in loss of sensitive data, incurring considerable financial damages. Therefore, towards securing smart cities from reply attacks, intrusion detection systems and frameworks based on deep learning have been proposed in the recent literature. However, the absence of the time dimension of these proposals is a major limitation. Therefore, we have developed a deep learning-based model for replay attack detection in smart cities. The novelty of the proposed methodology resides in the adoption of deep learning based models as an application for detecting replay attacks to improve detection accuracy. The performance of this model is evaluated by applying it to a real life smart city dataset, where replay attacks were simulated. Our results show that the proposed model is capable of distinguishing between normal and attack behaviours with relatively high accuracy. In addition, according to the results, our proposed model outperforms traditional classification and deep learning models. Last but not least, as an additional contribution, this paper presents a real life smart city data set with simulated replay attacks for future research. INDEX TERMS Smart cities, intrusion detection, replay attack, deep learning, convolutional neural networks.