The design and development of energy and spectrum-efficient solutions are important in the success of the Internet of Things (IoT). Due to the presence of an enormous number of smart devices, such as sensors, actuators, and different household devices achieving such scalable and efficient solutions are challenging. A wireless sensor network (WSN) with dynamic spectrum access (DSA) capability, known as the cognitive radio sensor network (CRSN) is recently introduced to deal with spectrum scarcity problem. Although the spectrum scarcity is reduced with DSA paradigm, the energy-efficient solutions are still required to be addressed due to the involvement of energy constrained devices in CSRN. Clustering is one of the efficient ways to optimize the energy consumption in the networks. Due to combination of both WSN and cognitive radio network (CRN), existing solutions of WSN and of CRNs are not applicable to CRSN. In this article, we propose a neighbor discovery algorithm and two greedy k-hop clustering schemes (k-SACB-WEC and k-SACB-EC) for CRSN with the aim focusing on IoT applications, which require constant intracluster and intercluster communications. We focus on achieving bichannel connectivity while maximizing network life. In our clustering different parameters, such as nodes' residual energy, spectrum awareness, appearance probability of primary users (PUs) of channels, channel quality, robustness on PUs' arrival, and the Euclidean distance between nodes are taken into consideration to select the hop count and common channels for clusters. Through simulation, we have highlighted the performance improvements of our proposed schemes in terms of the lifetime of the network, number of clusters, stability of networks, and frequency of reclustering over recently reported clustering algorithm in CRSN. The simulation results show that k-SACB-WEC generates at least 40% less number of clusters as compared to k-SACB-EC, network stability-aware clustering (NSAC), Prolong-SEP (PSEP), SAC-WCM, and Cognitive LEACH (CogLEACH). Also, in terms of network stability, the k-SACB-WEC achieves at least approximately 100% higher number of rounds before the first node dead than the compared competitive approaches.

Smart agriculture stands as a promising domain for IoT-enabled technologies, with the potential to elevate crop quality, quantity, and operational efficiency. However, implementing a smart agriculture system encounters challenges such as the high latency and bandwidth consumption linked to cloud computing, Internet disconnections in rural locales, and the imperative of cost efficiency for farmers. Addressing these hurdles, this paper advocates a fog-based smart agriculture infrastructure integrating edge computing and LoRa communication. We tackle farmers’ prime concern of animal intrusion by presenting a solution leveraging low-cost PIR sensors, cameras, and computer vision to detect intrusions and predict animal locations using an innovative algorithm. Our system detects intrusions pre-emptively, identifies intruders, forecasts their movements, and promptly alerts farmers. Additionally, we compare our proposed strategy with other approaches and measure their power consumptions, demonstrating significant energy savings afforded by our strategy. Experimental results highlight the effectiveness, energy efficiency, and cost-effectiveness of our system compared to state-of-the-art systems.

Real data plays a fundamental role in determining various features related to the collection site, such as monitoring, controlling, predictions, etc. Several systems in the Internet of Things (IoT) environment produce millions of data from the sensing node, and transmitting each of the data is costly in terms of bandwidth requirements, energy consumption, and protecting data from being corrupt or from potential threats such as man-in-the-middle attacks. In many environmental applications such as agriculture, the absolute change in the consecutive data points is usually very small (we call it slow changing environment). So, there is a need for a system that can predict the next data point within a predefined tolerable limit, then transmitting each data point can be avoided. To address this issue, we proposed an analytical prediction algorithm using estimations (APAEs). The algorithm is deployed and runs simultaneously in the three layers of architecture: sensing, fog, and cloud layers. The algorithm predicts the next data sensed by the sensor. If the difference between the actual sensed data point and the predicted data point is beyond the predefined tolerance, then the sensed value is sent to the fog node and further to the cloud; otherwise, the estimated value is accepted. We have implemented the proposed algorithm on a real testbed and also tested it on two data sets. We compare the amount of data points transmitted with the state of the state-of-the-art scheme. We also highlighted the reduction in energy consumption and high accuracy of our algorithm on the two data sets and a real testbed.

Indoor localization is defined as the process of locating a user or device in an indoor environment. It plays a crucial role for first responders in disaster and emergency situations. In situations where the environment does not change much after an incident happens, fingerprint based indoor localization can be used for effective localization and positioning. Due to the growth in smartphone users in the last few years, indoor localization using Wi-Fi fingerprints has been studied by researchers. The measured Wi-Fi signal strength can be used as an indication of the distribution of users in various indoor locations. In disaster and emergency situations, localization services should be highly accurate and fast. We can model localization as a classification problem and address using machine learning (ML) approaches. However, these two requirements are conflicting since an accurate fingerprint-based indoor localization system needs to process a large amount of data, and this leads to slow response. This problem becomes even worse when both the number of floors and the number of reference points increase. To address this challenge, we use a two-level localization in order to improve both the accuracy and the response time. First, the fingerprint database is used to train ML models. The localization phase has two steps: (i) floor prediction, and (ii) reference point prediction on the predicted floor. For floor prediction, we use K-Nearest Neighbors (KNN) classification algorithm. Then we use various ML models such as Random Forest, Decision Tree, and Support Vector Machine. We use a dataset having two files with different floor numbers. Experiment results showed that random forest gives the best accuracy among other ML models. So two-level localization method is more suitable than single level localization in terms of localization accuracy and speed, and thus can be utilized in many applications.

Cognitive radio networks (CRNs) involve an extensive exchange of control messages to coordinate spectrum sensing, medium access, routing, etc. Static control channel allocation uses preassigned common control channel (CCC) contrary to the opportunistic access paradigm. We propose a $k$-hop clustering scheme for the optimal CCC assignment problem ($k$-CCCP) for CRNs. We prove that the $k$-CCCP problem is NP-hard (nondeterministic polynomial-time hard) by reduction using minimum dominating set problem. We also found that the $k$-CCCP problem is approximable hard. We propose a $\ln \Delta$ approximation algorithm for the $k$-CCCP problem, where $\Delta$ is the largest cardinality among all $k$-hop neighborhood in a CRN. To account for primary user dynamics, we propose a distributed ranking algorithm for the $k$-CCCP problem with message complexity $\mathcal {O}(n \delta k)$, where $n, \delta$, and $k$ are number of nodes, the largest node degree, and hop parameter, respectively. To address the negative effect of multihop clustering, we also propose a mechanism to find the optimal $k$ value in multihop clustering. For performance comparison, extensive simulations are performed and the results show that our proposed scheme outperforms with the competitive schemes.