Delay and Reliability of Load-Based Listen-Before-Talk in LAA

© 2013 IEEE. With the release of the 5 GHz unlicensed spectrum has emerged licensed-Assisted access, in which long-Term evolution (LTE) operators compete with Wi-Fi users for a share of the unlicensed spectrum so as to augment their licensed spectrum. Subsequently, there has been the need to develop a LTE channel access mechanism that enables harmonious coexistence between Wi-Fi and LTE. Load-based listen-before-Talk (LB-LBT) has been adopted as this LTE channel access mechanism by the 3rd Generation Partnership Project (3GPP). Theoretical modelling of LB-LBT schemes has focused on throughput and fair channel-Time sharing between Wi-Fi and LTE technologies. We explore a LB-LBT scheme that belongs to LBT category 4, as recommended by the 3GPP, and develop a model for the distribution of the medium access control (MAC) delays experienced by the Wi-Fi packets and LTE frames. The model, validated by simulations, reveals design insights that can be used to dynamically adjust the LB-LBT parameters not only to achieve channel-Time fairness, but also to guarantee MAC-delay bounds, with specified probability

Energy efficient duty cycle design based on quantum immune clonal evolutionary algorithm in body area networks

© 2015 ICST. Duty cycle design is an important topic in body area networks. As small sensors are equipped with the limited power source, the extension of network lifetime is generally achieved by reducing the network energy consumption, for instance through duty cycle schemes. However, the duty cycle design is a highly complex NP-hard problem and its computational complexity is too high with exhaustive search algorithm for practical implementation. In order to extend the network lifetime, we proposed a novel quantum immune clonal evolutionary algorithm (QICEA) for duty cycle design while maintaining full coverage in the monitoring area. The QICEA is tested, and a performance comparison is made with simulated annealing (SA) and genetic algorithm (GA). Simulation results show that compared to the SA and the GA, the proposed QICEA can extending the lifetime of body area networks and enhancing the energy efficiency effectively

Unified Ciphertext-Policy weighted attribute-based encryption for sharing data in cloud computing

© 2018 by the authors. With the rapid development of cloud computing, it is playing an increasingly important role in data sharing. Meanwhile, attribute-based encryption (ABE) has been an effective way to share data securely in cloud computing. In real circumstances, there is often a mutual access sub-policy in different providers' access policies, and the significance of each attribute is usual diverse. In this paper, a secure and efficient data-sharing scheme in cloud computing, which is called unified ciphertext-policy weighted attribute-based encryption (UCP-WABE), is proposed. The weighted attribute authority assigns weights to attributes depending on their importance. The mutual information extractor extracts the mutual access sub-policy and generates the mutual information. Thus, UCP-WABE lowers the total encryption time cost of multiple providers. We prove that UCP-WABE is selectively secure on the basis of the security of ciphertext-policy weighted attribute-based encryption (CP-WABE). Additionally, the results of the implementation shows that UCP-WABE is efficient in terms of time

Low energy clustering in BAN based on fuzzy simulated evolutionary computation

© 2015 ICST. A low energy clustering method of body area networks based on fuzzy simulated evolutionary computation is proposed in this paper. To reduce communication energy consumption, we also designed a fuzzy controller to dynamically adjust the crossover and mutation probability. Simulations are conducted by using the proposed method, the clustering methods based on the particle swarm optimization and the method based on the quantum evolutionary algorithm. Results show that the energy consumption of the proposed method decreased compared with the other two methods, which means that the proposed method significantly improves the energy efficiency

Harmonising Coexistence of Machine Type Communications with Wi-Fi Data Traffic under Frame-Based LBT

© 1972-2012 IEEE. The existence of relatively long LTE data blocks within the licensed-assisted access (LAA) framework results in bursty machine-type communications (MTC) packet arrivals, which cause system performance degradation and present new challenges in Markov modeling. We develop an embedded Markov chain to characterize the dynamic behavior of the contention arising from bursty MTC and Wi-Fi data traffic in the LAA framework. Our theoretical model reveals a high-contention phenomenon caused by the bursty MTC traffic, and quantifies the resulting performance degradation for both MTC and Wi-Fi data traffic. The Markov model is further developed to evaluate three potential solutions aiming to alleviate the contention. Our analysis shows that simply expanding the contention window, although successful in reducing congestion, may cause unacceptable MTC data loss. A TDMA scheme instead achieves better MTC packet delivery and overall throughput, but requires centralized coordination. We propose a distributed scheme that randomly spreads the MTC access processes through the available time period. Our model results, validated by simulations, demonstrate that the random spreading solution achieves a near TDMA performance, while preserving the distributed nature of the Wi-Fi protocol. It alleviates the MTC traffic contention and improves the overall throughput by up to 10%

A Sybil attack detection scheme for a forest wildfire monitoring application

© 2016 Elsevier B.V. Wireless Sensor Networks (WSNs) have experienced phenomenal growth over the past decade. They are typically deployed in human-inaccessible terrains to monitor and collect time-critical and delay-sensitive events. There have been several studies on the use of WSN in different applications. All such studies have mainly focused on Quality of Service (QoS) parameters such as delay, loss, jitter, etc. of the sensed data. Security provisioning is also an important and challenging task lacking in all previous studies. In this paper, we propose a Sybil attack detection scheme for a cluster-based hierarchical network mainly deployed to monitor forest wildfire. We propose a two-tier detection scheme. Initially, Sybil nodes and their forged identities are detected by high-energy nodes. However, if one or more identities of a Sybil node sneak through the detection process, they are ultimately detected by the two base stations. After Sybil attack detection, an optimal percentage of cluster heads are elected and each one is informed using nomination packets. Each nomination packet contains the identity of an elected cluster head and an end user's specific query for data collection within a cluster. These queries are user-centric, on-demand and adaptive to an end user requirement. The undetected identities of Sybil nodes reside in one or more clusters. Their goal is to transmit high false-negative alerts to an end user for diverting attention to those geographical regions which are less vulnerable to a wildfire. Our proposed approach has better network lifetime due to efficient sleep–awake scheduling, higher detection rate and low false-negative rate

A sybil attack detection scheme for a centralized clustering-based hierarchical network

© 2015 IEEE. Wireless Sensor Networks (WSNs) have experienced phenomenal growth over the past decade. They are typically deployed in remote and hostile environments for monitoring applications and data collection. Miniature sensor nodes collaborate with each other to provide information on an unprecedented temporal and spatial scale. The resource-constrained nature of sensor nodes along with human-inaccessible terrains poses various security challenges to these networks at different layers. In this paper, we propose a novel detection scheme for Sybil attack in a centralized clustering-based hierarchical network. Sybil nodes are detected prior to cluster formation to prevent their forged identities from participating in cluster head selection. Only legitimate nodes are elected as cluster heads to enhance utilization of the resources. The proposed scheme requires collaboration of any two high energy nodes to analyze received signal strengths of neighboring nodes. The simulation results show that our proposed scheme significantly improves network lifetime in comparison with existing clustering-based hierarchical routing protocols

Energy Efficient and Reliable ARQ Scheme (ER-ACK) for Mission Critical M2M/IoT Services

Wireless sensor networks (WSNs) are the main infrastructure for machine to machine (M2M) and Internet of thing (IoT). Since various sophisticated M2M/IoT services have their own quality-of-service (QoS) requirements, reliable data transmission in WSNs is becoming more important. However, WSNs have strict constraints on resources due to the crowded wireless frequency, which results in high collision probability. Therefore a more efficient data delivering scheme that minimizes both the transmission delay and energy consumption is required. This paper proposes energy efficient and reliable data transmission ARQ scheme, called energy efficient and reliable ACK (ER-ACK), to minimize transmission delay and energy consumption at the same time. The proposed scheme has three aspects of advantages compared to the legacy ARQ schemes such as ACK, NACK and implicit-ACK (I-ACK). It consumes smaller energy than ACK, has smaller transmission delay than NACK, and prevents the duplicated retransmission problem of I-ACK. In addition, resource considered reliability (RCR) is suggested to quantify the improvement of the proposed scheme, and mathematical analysis of the transmission delay and energy consumption are also presented. The simulation results show that the ER-ACK scheme achieves high RCR by significantly reducing transmission delay and energy consumption

Modified elite chaotic artificial fish swarm algorithm for PAPR reduction in OFDM systems

© 2014 IEEE. Orthogonal frequency division multiplexing (OFDM) is a leading technology in the field of broadband wireless communications. In OFDM systems, a high peak-to-average power ratio (PAPR) is a critical issue, which may cause a nonlinear distortion and reduce power efficiency. To reduce the PAPR, partial transmit sequences (PTS) technique can be applied to the transmit data. However, the phase factor sequence selection in PTS technique is a non-linear optimization problem and it suffers from high complexity and memory use when there is a large number of non-overlapping sub-blocks in one symbol. In this paper a novel modified elite chaotic artificial fish swarm algorithm for PTS method (MECAFSA-PTS) is proposed to generate the optimum phase factors. The MECAFSA-PTS method is evaluated with extensive simulations and its performance is compared with quantum evolutionary and selective mapping algorithms. Our results show that the proposed MECAFSA-PTS algorithm is efficient in PAPR reduction