A critical design issue for wireless sensor networks (WSNs) is the development of medium access control (MAC) protocols that efficiently reduce power consumption. WSNs sensor nodes are generally powered by batteries which provide a limited amount of energy, and it is often difficult to recharge or replace batteries. Therefore power aware and energy efficient MAC protocols at each layer of the communications are very essential for wireless sensor networks (11). Fairness to both the usage of a channel and messages may also be traded as for improved power consumptions. In case of classical antennas, unfair channel allocation and wastage of channels between each node can be happened, which is directly affects throughput performance. On the other hand these can bring a problem such as MAC-deadlock, hidden and exposed terminal problem. To overcome these problems a directional antennas have been extensively used in designing MAC protocols for wireless sensor networks. Directional antennas provide many advantages over the classical antennas. These advantages include spatial reuse channel and increases in coverage range distance (9). One of the main considerations in designing MAC protocols for static wireless sensor networks is to reduce power consumption at the sensor nodes. This is usually done by imposing transmission and receiving schedules on the sensor nodes from only one side at same time. Since it is desirable for a sensor network to be self managed, these schedules need to be worked out by individual nodes in a distributed fashion. In this paper, we show that directional antennas can be used effectively to solve a common hidden and exposed terminal problem by using an energy efficient MAC protocol for wireless sensor networks. This directional Antenna could be rotated in case of base station node to avoid directional hidden terminal problem. Our MAC protocol conserves energy at the nodes by calculating a scheduling strategy at individual nodes and by avoiding packet collisions almost completely.

Mobile computing is one of the significant opportunities that can be used for various practical applications in numerous fields in real life. Due to inherent characteristics of ubiquitous computing, devices can gather numerous types of data that led to innovative applications in many fields with a unique emerging prototype known as Crowd sensing. Here, the involvement of people is one of the important features and their mobility provides an exclusive opportunity to collect and transmit the data over a substantial geographical area. Thus, we put forward novel idea about Quality of Information (QOI) with unique parameters with opportunistic uniqueness of people’s mobility in terms of sensing and transmission. Additionally, we propose some of the viable improved ideas about the competent opportunistic data collection through efficient techniques. This work also considered some of the open issues mentioned by previous related works.

A software-defined network (SDN) brings a lot of advantages to the world of networking through flexibility and centralized management; however, this centralized control makes it susceptible to different types of attacks. Distributed denial of service (DDoS) is one of the most dangerous attacks that are frequently launched against the controller to put it out of service. This work takes the special ability of SDN to propose a solution that is an implementation run at the multicontroller to detect a DDoS attack at the early stage. This method not only detects the attacks but also identifies the attacking paths and starts a mitigation process to provide protection for the network devices. This method is based on the entropy variation of the destination host targeted with its IP address and can detect the attack within the first 250 packets of malicious traffic attacking a particular host. Then, fine-grained packet-based detection is performed using a deep-learning model to classify the attack into different types of attack categories. Lastly, the controller sends the updated traffic information to neighbor controllers. The chi-squared ( x 2 ) test feature selection algorithm was also employed to reveal the most relevant features that scored the highest in the provided data set. The experiment result demonstrated that the proposed Long Short-Term Memory (LSTM) model achieved an accuracy of up to 99.42% using the data set CICDDoS2019, which has the potential to detect and classify the DDoS attack traffic effectively in the multicontroller SDN environment. In this regard, it has an enhanced accuracy level to 0.42% compared with the RNN-AE model with data set CICDDoS2019, while it has improved up to 0.44% in comparison with the CNN model with the different data set ICICDDoS2017.

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