Wireless Networks

The evolution of mobile network and the popularization of mobile devices; the demand for multimedia services and 3D graphics applications on limited resource devices is more contemporary. Most of the works on multimedia transmission are focused on bit errors and packet losses due to the fading channel environment of a wireless network. Error resilient multimedia is significant research topic which can be adapted to the different conditions in a wireless environment. The current solutions in transmission of multimedia across different networks include some type of transcoder where the source is partially or fully decoded, and re-encoded to suit the network conditions. This paper introduces a flexible progressive coding framework for 3D meshes, which can be adapted to the different conditions imposed by wired and wireless channels at the bitstream level. By avoiding the computationally complex steps of transcoding between networks, could deteriorate decoded model quality. The framework also allows refined degradation of model quality when the network conditions are poor due to congestion or deep fades.

Acquiring real time sensory data using remote swarms of tiny sensors depends on efficient wireless networking. Often, battery longevity of the sensors is a critical design requirement, which makes long-range RF transmission inadequate for this task. A common solution is to use short-range and energy-efficient RF protocols within the swarm, then communicate with the outside world via a capable agent or a networking gateway. In this paper we suggest an alternative networking model, which enables the swarm to operate independently, without the assistance of an intermediate proxy. Specifically, we present a networking model in which the sensors in the swarm transmit data using energy-efficient free space optical links (FSO). We discuss the details of aiming optical transmission interfaces in the directions of distant sinks, such as antenna towers, drones and even LEO-satellites. This FSO-based network architecture poses various data collection problems, which are related to the directional nature of optical transmitters and receivers. Using simulations we address several such problems, and demonstrate the viability of the model for sensory data collection.

Sự ổn định trong các đặc trưng hàng đợi với công suất trung bình và tối đa hóa việc xử lý dữ liệu là một vấn đề nghiên cứu nổi bật trong bất kỳ mạng lưới nào. Điều này cần được đảm bảo ngay cả trong các mô hình Điện toán Biên Di động (MEC), nơi việc tích hợp liền mạch và liên tục các ứng dụng đổi mới được cung cấp với độ trễ thấp và chất lượng cải tiến. Một kế hoạch giải phóng là cần thiết để đảm bảo điều này với sự điều chỉnh công suất xử lý và chi phí hoạt động mạng tại các nút biên, điều này sẽ cải thiện hiệu quả của IoT với điện toán biên. Một thuật toán giải phóng dựa trên nhiệm vụ được đề xuất trong bài báo này nhằm đưa ra các quyết định thông minh hơn cho việc phân bổ tài nguyên và tăng cường khả năng tính toán đến máy chủ MEC bằng cách cho phép thiết bị di động (MD) giải phóng các tác vụ tính toán nặng về các Multi-eNodeB (Multi-eNB) gần nhất dựa trên trạng thái kênh biến đổi theo thời gian mong muốn. Một mô hình thiết bị MEC không dây đang được nhiều người dùng sử dụng để phát sóng tác vụ của họ được xem xét. Sự ổn định của hàng đợi và việc giảm thiểu năng lượng tiêu thụ trong khi tối đa hóa phần thưởng ngay cả dưới thời hạn hạn chế được duy trì bởi khung phụ đề mới do chúng tôi đề xuất, có tên là DCARL–ARP (Học Tăng Cường Chú Ý Tích Chập Sâu - Chính Sách Thưởng Thích Ứng), kết hợp giữa tối ưu hóa Tích Chập Sâu và tối ưu hóa Lyapunov với cơ chế tập trung chú ý bản đồ đặc trưng. DCARL–ARP được tích hợp để đưa ra quyết định chọn lựa trạng thái hợp lý trong các khoảng thời gian khác nhau. Phân tích và đánh giá hiệu suất chứng minh rằng DCARL–ARP có tỷ lệ tính toán tối ưu thích hợp cho việc thực hiện thời gian thực trong các điều kiện kênh biến đổi. Đánh giá thực nghiệm cho thấy cơ chế này có thể giảm hiệu quả mức tiêu thụ năng lượng trung bình cho việc thực hiện lên đến 0.02% và chiều dài hàng đợi dữ liệu trung bình lên tới 50%.

Congestion control is necessary for enhancing the quality of service in wireless sensor networks (WSNs). With advances in sensing technology, a substantial amount of data traversing a WSN can easily cause congestion, especially given limited resources. As a consequence, network throughput decreases due to significant packet loss and increased delays. Moreover, congestion not only adversely affects the data traffic and transmission success rate but also excessively dissipates energy, which in turn reduces the sensor node and, hence, network lifespans. A typical congestion control strategy was designed to address congestion due to transient events. However, on many occasions, congestion was caused by repeated anomalies and, as a consequence, persisted for an extended period. This paper thus proposes a congestion control strategy that can eliminate both types of congestion. The study adopted a fuzzy logic algorithm for resolving congestion in three key areas: optimal path selection, traffic rate adjustment that incorporates a momentum indicator, and an optimal timeout setting for a circuit breaker to limit persistent congestion. With fuzzy logic, decisions can be made efficiently based on probabilistic weights derived from fitness functions of congestion-relevant parameters. The simulation and experimental results reported herein demonstrate that the proposed strategy outperforms state-of-the-art strategies in terms of the traffic rate, transmission delay, queue utilization, and energy efficiency.

In this paper a miniaturized low pass filter (LPF) with 2.5 GHz cut-off frequency and a novel compact, harmonics suppressed Wilkinson power divider (WPD) at 0.7 GHz is proposed. The proposed divider consists of two multi-stub LPFs and three open stubs at each port. The presented open stub at port one suppresses the second harmonic and other two open stubs at output ports, suppress the third harmonic. To suppress high order harmonics a novel 12 sections LPF based on aperiodic stub is proposed. This filter is designed to suppressed 4th to 15th harmonics. The cut off frequency of applied filter is 2.5 GHz, which creates 12 transmission zeros and suppresses corresponding 4th–15th harmonics of the proposed divider. The proposed WPD not only has perfect harmonics suppression, but also extremely decreases the circuit size. The overall size of the fabricated divider is only 0.116 λg × 0.044 λg, which shows more than 73% size reductions, compared to the 0.7 GHz conventional WPD.

Due to the mobility of node and different spectrum availability pattern, CR networks are frequently divided into unpredictable partitions. Usually, these partitions are irregularly connected; hence, secure and reliable routing becomes major issue for these types of network. In order to overcome these issues, we propose a secure and reliable routing in CRN based on distributed Boltzmann–Gibbs learning algorithm. This algorithm is implemented for relay node selection phase. In addition, the authentication is done based on secure routing distributed Boltzmann–Gibbs learning algorithm. We consider the metrics such as trust value and total delay for the successful and reliable transmission of the packet. Also, in order to increase the reliability, we implement LDPC code at the time of relay node selection phase. The proposed code helps to cancel any kind of electronic interference and channel noise interference.

This paper describes a tool created to simulate the effects of evaporation ducts on wireless communication networks which are close to the sea surface. The tool is an extension of the ns-3 network simulator and implements two propagation loss models: Two Ray, which models reflection effects; and Three Ray, which models the effects of both reflection and refraction. As the Three Ray model has the evaporation duct height as one of its parameters, a model to estimate this height based on weather conditions (meteorological measurements) was also implemented. The tool allows us to show how signal is weakened with the distance from the transmitter to the receiver, under different evaporation duct heights. It has been of increading interest to model these effects due to the importance of wireless networks located over the sea surface for supporting different types of applications, such as surveillance, security and ecological monitoring. Extending ns-3 is of particular importance, as it is one of the most largely used simulators for designing and evaluating communication networks.

The increasing demand for real-time applications of Wireless Sensor Networks (WSNs) makes Quality of Service (QoS)-based charging scheduling models an interesting and hot research topic. Satisfying QoS requirements (e.g. data collection integrity, charging respond delay, etc.) for the different applications of WSNs raises significant challenges. More precisely, an effective scheduling strategy not only needs to improve the charging efficiency of charging vehicles but also needs to reduce the charging respond delay of the requests to be charged, all of which must be based on the integrity of data collection. For such applications, existing studies on charging issue often deployed one or more mobile vehicles, which have deficiencies in practical applications. On one hand, it usually is insufficient to employ just one vehicle to charge many sensors in a large-scale application scenario due to the limited battery capacity of the charging vehicle or energy depletion of some sensors before the arrival of the charging vehicle. On the other hand, while the collaboration between multiple vehicles for large-scale WSNs can significantly increase charging capacity, the cost is too high in terms of the initial investment and maintenance costs of these vehicles. To overcome these deficits, in this work, we propose a novel QoS-based on-demand charge scheduling (abbreviated shortly as QOCS) model that one charging vehicle carries multiple removable battery powered chargers. In the novel QoS-based charging model, we study the charging scheduling problem of requesting nodes to guarantee the integrity of network data collection and maximize the satisfaction of charging services. In the QOCS model, We jointly consider the coverage contribution and energy urgency to sort the charging requests of sensors, and introduce a hybrid power supply mechanism based on supply and demand to improve energy utilization. We evaluate the performance of the proposed model through extensive simulation and experimental results show that our model achieves better performance than existing methods.

In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent’s itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs near-optimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool.

Ambient Intelligence deployments are very vulnerable to Cyber-Physical attacks. In these attacking strategies, intruders try to manipulate the behavior of the global system by affecting some key elements within the deployment. Typically, attackers inject false information, integrate malicious devices within the deployment, or infect communications among sensor nodes, among other possibilities. To protect Ambient Intelligence deployments against these attacks, complex data analysis algorithms are usually employed in the cloud to remove anomalous information from historical series. However, this approach presents two main problems. First, it requires all Ambient Intelligence systems to be networked and connected to the cloud. But most new applications for Ambient Intelligence are supported by isolated systems. And second, they are computationally heavy and not compatible with new decentralized architectures. Therefore, in this paper we propose a new decentralized security solution, based on a Blockchain ledger, to protect isolated Ambient Intelligence deployments. In this ledger, new sensing data are considered transactions that must be validated by edge managers, which operate a Blockchain network. This validation is based on reputation metrics evaluated by sensor nodes using historical network data and identity parameters. Through information theory, the coherence of all transactions with the behavior of the historical deployment is also analyzed and considered in the validation algorithm. The relevance of edge managers in the Blockchain network is also weighted considering the knowledge they have about the deployment. An experimental validation, supported by simulation tools and scenarios, is also described. Results show that up to 93% of Cyber-Physical attacks are correctly detected and stopped, with a maximum delay of 37 s.