Cooperative Overhead Research Articles

Two-Layers Workspace: A New Approach to Cooperative Object Transportation With Obstacle Avoidance for Multi-Robot System

In this paper we present a new approach to cooperative overhead object transportation with obstacle avoidance for multi-robot systems. This approach considers situations in which there are obstacles that only pose a risk to the robots involved in the transport and not necessarily to the group of robots with the load. Thus, we propose to separate the workspace into two layers in a way that the obstacles which only pose risk of collision to the robots can be transposed by a local planner. This new approach to the load transportation problem allows the system to find shorter paths or even find a solution in situations which would not be possible with the classic approach. Furthermore, the developed method has a configuration space independent of the number of robots and it is robust to environmental variation, not reducing its performance in more complex environments as in narrow passages or long corridors. The proposed approach was tested by simulation and the results are compared to the classic one.

Intelligent clustering cooperative spectrum sensing based on Bayesian learning for cognitive radio network

In cognitive radio network, cooperative spectrum sensing (CSS) can improve the sensing performance on the absence of a primary user, when the sensing channel is in severe fading and shadowing effect. However, CSS is sensitive to the fading reporting channel. In this paper, an intelligent clustering CSS based on Bayesian learning is proposed to improve sensing performance under both perfect and imperfect sensing reports as well as decrease the rate loss and cooperative overhead. The clustering CSS is performed by intra-cluster CSS and inter-cluster CSS. An optimal sensing threshold for the intra-cluster CSS is achieved by minimizing the total Bayesian cost. The total false alarm probability and detection probability for the inter-cluster CSS are obtained by the Bayesian fusion. A clustering algorithm based on K-means learning is proposed to classify the sensing nodes and select the cluster heads. The simulation results have shown that the proposed clustering CSS outperforms the traditional CSS without clustering in the aspects of sensing performance and time overhead.

A Quantization-Based Multibit Data Fusion Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks.

Spectrum sensing remains a challenge in the context of cognitive radio networks (CRNs). Compared with traditional single-user sensing, cooperative spectrum sensing (CSS) exploits multiuser diversity to overcome channel fading, shadowing, and hidden terminal problems, which can effectively enhance the sensing performance and protect licensed users from harmful interference. However, for a large number of sensing nodes that need high bandwidth of the control channel for data transmitting, CSS increases cooperative overhead. To address this problem, we investigated the soft decision fusion strategy under a limited bandwidth of the control channel and proposed a simple quantization-based multibit data soft fusion rule for CSS for its simple structure and easily implementation. Under the quantization-based sensing strategy, each cooperative secondary user (SU) adopts an energy detector for local spectrum sensing. Each SU transmits quantized multibit data that sends local sensing information, instead of forwarding local one-bit hard decision results or original observation statistics, to the fusion center (FC). Furthermore, the closed-form expressions of the quantization levels and the quantization thresholds are analytically derived. Simulation results indicate that the detection performance of the proposed method approaches that of the conventional soft fusion rule with less cooperative overhead and outperforms the hard decision rules. Extensive simulations also show that multibit quantization fusion achieves a desirable tradeoff between the sensing performance and the control channel overhead for CSS.

Mitigation strategy against SSDF attack for healthcare in cognitive radio networks

Wireless communications have a rapid growth that leads to huge demand on the deployment of new wireless services in both licensed and unlicensed frequency spectrum. The performance of Cooperative Spectrum Sensing (CSS) in cognitive radio is based on two factors: cooperative gain and cooperative overhead. There are many other dominating factors that affect the performance of CSS like the presence of attacks, energy efficiency, sensing time and delay, spectrum efficiency, etc. The major threat to CSS is Spectrum Sensing Data Falsification (SSDF) attack. Existing methods to mitigate the SSDF attack doesn't focus on energy efficiency which is performed here. To deal with these challenges, we propose an energy efficient mechanism against SSDF attack for healthcare application in Cognitive Radio Networks (CRNs). We propose a Malicious Node Identification Algorithm (MIA) to mitigate the problem of SSDF attack in the network and to eliminate the redundant data at the fusion centre, Balanced Cluster-based Redundancy Check Aggregation (BCRCA) mechanism is proposed. Finally, the decision taken by the fusion centre is based on Weighted Selection Combining (WSC) technique that improves spectrum decision accuracy.

Bandwidth allocation-based simultaneous cooperative spectrum sensing and energy harvesting for multicarrier cognitive radio

In cognitive radio (CR), a secondary user (SU) may consume more energy due to spectrum sensing. In order to guarantee transmission performance of the SU, a bandwidth allocation-based simultaneous cooperative spectrum sensing and energy harvesting for multicarrier CR is proposed, which can collect the radio frequency (RF) energy of the wireless signal from primary user (PU) to supply the consumed sensing energy. Each subcarrier is split into spectrum sensing bandwidth, energy harvesting bandwidth and transmission bandwidth, thus cooperative spectrum sensing, energy harvesting and data transmission can be implemented simultaneously within transmission time. Multicarrier cooperative spectrum sensing is proposed to improve sensing performance and decrease cooperative overhead through combining the sensing results from all the subcarriers. A joint optimization problem has been formulated to achieve the maximal throughput of the SU by jointly optimizing sensing bandwidth ratio and subcarrier power. Simulation results have shown that there exists an optimal sensing bandwidth that maximizes the throughput and the performance of the proposed model is predominant.

Protection Against Defecting Attack and Enhanced Channel Allocation for Video Streaming in CRN

Arising issue of spectrum scarcity is resolved by emerging cognitive radio technology. In which, unoccupied portion of licensed spectrum would be granted for unlicensed user in order to upgrade the spectrum utilization. Spectrum sensing is an enabling task for labeling the spectrum holes and away from spectrum congestion in cognitive radio networks. Impact of secondary user’s cooperation developed cooperative spectrum sensing for effective spectrum sensing, many secondary users are to be associated in sensing the idle channels. Adversary secondary users (SU) mimic as primary user in cooperative sensing scheme, called primary user emulation attack (PUEA). If any SU forges the sensing data, that is known as spectrum sensing data falsification attack (SSDF). Rescuing the network against these two attacks is the major issue of cognitive radio networks. To address these challenges, we propose cognitive radio network to ensure security in cooperative spectrum sensing for video streaming application. Sensing based clustering algorithm assembles the cognitive radios into clusters to mitigate cooperative overhead and consumed energy. In order to avert PUEA attacks, we introduce authentication signature ID algorithm which provides authority for licensed user to use spectrum. To diminish SSDF attacks, we exploit Hamming algorithm to abolish forgery node from spectrum sensing decision. Furthermore PRN channel allocation algorithm is employed to precisely deliver the video with high quality and lower delay. For video transmission in cognitive radio network, we need to compress the video frames by block truncation coding-pattern fitting in order to reduce bandwidth consumption. This compression technique offers good video transmission with high compression ratio in cognitive radio network, due to trade-off among quality, bit rate and decoding time. Our proposed framework demonstrates the simulation with improved detection rate of idle channel and reduced the delay for high quality video transmission.

Secure Channel Allocation Approach for Video Streaming in Cognitive Radio Networks

In this paper, we propose a technique to ensure security in cooperative spectrum sensing for video streaming application in cognitive radio network. Sensing based clustering algorithm puts together the cognitive radios into clusters to mitigate cooperative overhead and to reduce energy consumption. In order to avert primary user emulation attacks, we introduce authentication signature ID algorithm which provides authority for the licensed user to use the spectrum. To diminish spectrum sensing data falsification attacks, we exploit hamming algorithm to expel forgery node from spectrum sensing decision. Furthermore, the primary radio network channel allocation algorithm is employed to precisely deliver the video with high quality and lower delay. For video transmission in a cognitive radio network, we compress the video frames using block truncation coding-pattern fitting in order to reduce bandwidth requirement. This compression technique offers good video transmission with a high compression ratio in a cognitive radio network, due to trade-off among quality, bit rate and decoding time. Simulation results reveal that there is a significant improvement in the detection rate of idle channel and reduction of delay for high-quality video transmission.

Development and Demonstration of Graphical User Interface Spectrum Sensing Algorithm using some Wireless Systems in South Africa

The wireless communication industry using radio spectrum is recently going through major innovations and advancements. With this transformation, the demand for and usage of radio spectrum has increased exponentially making radio spectrum indeed a scarce natural resource. In order to solve this problem, the possibility of opening up the unused portions of licensed spectrum by sharing using cognitive radio technology has been in the spotlight for maximizing radio spectrum utilization as well to as ensure sufficient radio spectrum availability for future wireless services and applications. With this objective in mind, this paper looks at the principles and technologies of cooperative spectrum sensing in cognitive radio environment in improving radio spectrum utilization. The paper provides a comprehensive review on spectrum sensing as a key functional requirement for cognitive radio technology by focusing on its application on dynamic spectrum access that enables unused portions of licensed spectrum to be used in an opportunistic manner as long as the operation of the unlicensed user will not affect that of the licensed user. In satisfying this dynamic spectrum access requirement, a friendly interactive graphical user interface (GUI) spectrum sensing application program was developed. The detail activities involve in the development of the application program, also known as spectrum sensing and detection algorithm (SSADA), was fully documented and presented in the paper. The developed graphical user interface application program after successfully developed was evaluated. The performance evaluations of developed graphical user interface sensing algorithm show that the algorithm performs favourably well. The program overall evaluation results provide bedrock information on how to improve cooperative spectrum sensing gain without incurring a cooperative overhead.

Optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing in cognitive radio network

In order to improve the throughput of cognitive radio (CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple users and one fusion center. The frame structure of cooperative spectrum sensing was divided into multiple transmission time slots and one sensing time slot consisting of local energy detection and cooperative overhead. An optimization problem was formulated to maximize the throughput of CR network, subject to the constraints of both false alarm probability and detection probability. A joint optimization algorithm of sensing time and number of users was proposed to solve this optimization problem with low time complexity. An allocation algorithm of cooperative users was proposed to preferentially allocate the users to the channels with high utilization probability. The simulation results show that the significant improvement on the throughput can be achieved through the proposed joint optimization and allocation algorithms.

Joint optimisation algorithm of cooperative spectrum sensing with cooperative overhead and sub‐band transmission power for wideband cognitive radio network

AbstractCognitive radio (CR) allows a secondary user (SU) to utilise the spectrum allocated to the primary user (PU) on the premise of detecting the absence of PU by spectrum sensing. The SU often adopts cooperative spectrum sensing to cope with the hidden terminal problem if the PU is shadowed or in serious multi‐path fading; however, the cooperative overhead may decrease the throughput of the SU. In this paper, we consider a wideband CR network that can reuse multiple idle sub‐bands after cooperative spectrum sensing and then analyse the total throughput of the SU over all the sub‐bands. A multi‐variable nonconvex optimisation problem of cooperative spectrum sensing with cooperative overhead and sub‐band transmission power is proposed, which maximises the total throughput of the SU subject to the constraints of the false‐alarm probability, the detection probability, the cooperative overhead and the maximal total power of the SU. For obtaining the optimal solution, we divide the optimisation problem into two sub‐optimisation problems: one for optimising the cooperative spectrum sensing including the sensing time and the number of SUs and the other one for optimising the sub‐band transmission power. We have proven the convex optimisation of the two sub‐optimisation problems and proposed a joint optimisation algorithm of the sensing time, the number of SUs and the sub‐band transmission power. The simulation results show that the proposed joint optimisation algorithm can achieve the largest throughput with the false‐alarm probability and the detection probability within their limits. Copyright © 2013 John Wiley & Sons, Ltd.