This study presents a novel and resilient Intrusion Detection System (IDS) designed to address the various challenges posed by Internet of Things (IoT) systems. The proposed model utilizes a Deep Belief Network (DBN) for feature selection in conjunction with Mean Shift clustering, enhancing detection accuracy. Additionally, the system utilizes adaptive drift detection to efficiently manage high-dimensional and dynamic IoT networks. Evaluation of the IoT20 dataset demonstrates that the proposed DBN IDS achieves a Root Mean Square Error (RMSE) of 0.025, with a precision of 97.8% and a recall of 98.1%. The model also boasts an F1 score of 97.9% and a specificity of 98.7%. Qualitative analysis reveals an Area Under the Curve (AUC) of 0.98 for the Receiver Operating Characteristic (ROC) curves, indicating near-perfect classification performance. Other performance metrics of the IDS are favourable, with reconstruction error limiting factors reported at 0.022, suggesting a reliable model for normal behaviour and accurate anomaly detection. The model’s accuracy for inference and evaluation stands at 96% and 97%, respectively, illustrating the consistency of the studied model across different data segregations. These results highlight that the proposed DBN-based IDS is a reliable solution that goes beyond current threats in the IoT landscape, effectively detecting both existing and emerging threats.

The cloud-fog computing paradigm is a novel hybrid computing model that delivers computational services to Fog nodes situated near data sources. This paradigm features a volatile and dynamic network topology, comprising heterogeneous IoT devices with varying computational capabilities, alongside a large number of diverse end-user requests. These complexities present significant challenges for researchers in establishing a robust, energy-efficient, and reliable communication environment. Efficient and optimal task scheduling is among these challenges, as it involves finding appropriate computing resources for processing tasks. Assigning tasks to fog nodes reduces delay but increases energy consumption, while routing tasks to cloud servers conserves energy but prolongs transmission delay. Therefore, it is essential to develop an optimal task scheduling algorithm for a reliable, delay-efficient, and energy-efficient communication environment. To address this, we propose a Multi-objective Harris Hawks Optimization (HHO)-based task scheduling algorithm (MoHHOTS) for cloud-fog computing networks, aiming to optimize task scheduling with the objectives of minimizing delay and energy consumption. MoHHOTS is implemented in MATLAB and evaluated against state-of-the-art benchmark algorithms, including MOGWO and the cloud-fog cooperation algorithm. Leveraging the high convergence and stochastic operators of the HHO algorithm, alongside a balanced approach to iteration between diversification and intensification, the proposed algorithm provides a set of trade-off solutions via the Pareto-optimal Front. Simulation results demonstrate the efficacy of the proposed solution, achieving improvements of up to 25% over a similar scheduling algorithm in terms of optimizing transmission delay and energy consumption.

Vehicular ad-hoc network (VANET) is highly dynamic due to the high speed and sparse distribution of vehicles on the road. This creates major challenges (e.g., network fragmentation, packet routing) for the researchers to enable robust, reliable, and scalable communication, especially in a highly dense network. Clustering in VANET is one of the remedies to address the scalability issue. However, it is observed in the literature, that existing clustering techniques produce a high number of clusters for the vehicular environment. Consequently, it increases the consumption of scarce resources in a wireless network. Furthermore, it also increases the communication overhead as well as the number of hops for data routing. As a result communication latency also increases and the reliability of communication protocol decreases. So it is highly desirable to find out the optimal clusters for a given vehicular environment. As finding optimal clusters is a multi-objective combinatorial optimization problem, therefore by employing nature-inspired meta-heuristic algorithms we can optimize the multi-objective problem. To this end, we proposed a novel clustering algorithm based on the Harris Hawks Optimization (HHO) algorithm for VANET (HHOCNET). HHO algorithm is a nature-inspired meta-heuristic algorithm inspired by the foraging maneuver of hawks called surprise pounce. The proposed framework imitates the cooperative foraging maneuver of hawks (i.e., surprise pounce for creating optimized vehicular clusters). The stochastic operators of the HHO algorithm and proper maintenance of the equilibrium state between the operations of exploration and exploitation enable the proposed algorithm to escape from the local optima and provide a globally optimal solution (i.e., the optimal number of vehicular clusters). Simulations are performed in MATLAB and the results are compared with the state-of-art schemes (i.e., Gray Wolf optimization-based clustering algorithm (GWOCNET), Multi-objective Particle Swarm Optimization (MO, PSO), and Comprehensive Learning Particle Swarm Optimization (CLPSO)) using different performance metrics. The results demonstrate that the proposed approach is an effective approach for clustering in VANET and outer performs the other benchmark algorithms in terms of optimizing the multi-objective clustering problem. HHOCNET algorithm selects 36.04% of nodes as cluster heads while the existing state-of-the-art schemes are providing 50.42%, 56.7%, and 60.89% for GWOCNET, CLPSO, and Multi-objective Particle Swarm Optimization (MOPSO). The proposed HHOCNET algorithm enhances the performance of the vehicular network by up to 15%. Consequently, it increases network efficiency by reducing the consumption of the required wireless resources. It also reduces the number of hops for packet routing. Hence it achieves a minimum end-to-end communication latency.

Recently, the simultaneous usage of various wireless technologies, such as Wi-Fi, Bluetooth, ZigBee and so on, becomes common and the propensity tends to be more complicated with the prevalence of smart and IoT devices. Such technology increments, however, intensify challenging Cross-Technology Interference (CTI) issues in a shared wireless band. To circumvent the hurdle, we propose a way which minimizes the medium occupation of wireless devices, called GreenAir. In the range of preserving network performances like connectivity and delay, GreenAir supports to generate the lesser communication links among homogeneous devices by utilizing a duty-cycle control. We experiment whether such approach is feasible to reduce CTI under the mixture of heterogeneous devices. Through retransmission and throughput measures in the combined use of Wi-Fi, ZigBee and Microwave oven (aggressive offender in ISM band), we validate that there exist large margins to reduce CTI via OPNET modeler. For the better applicability in IoT surroundings where the addition/removal of devices is arbitrary and frequent, we further tailor the operation of GreenAir to be distributed and autonomous. As our approach only requires local communications among homogeneous devices, the usability of GreenAir is device-agnostic.

Dense Wi-Fi and Bluetooth (BT) environments become increasingly common so that the coexistence issue between Wi-Fi and BT is imperative to solve. In this paper, we propose BlueCoDE, a coordination scheme for multiple neighboring BT piconets, to make them collision-free and less harmful to Wi-Fi. BlueCoDE reuses BT's existing PHY and MAC design, thus making it practically feasible. We implement a prototype of BlueCoDE on Ubertooth One platform and corroborate the performance gain via analysis, NS-3 simulations, and prototype-based experiments. Our experimental results show that with merely 10 legacy BT piconets, neighboring Wi-Fi network becomes useless achieving under 1 Mb/s throughput, while BlueCoDE enables the Wi-Fi throughput always remain above 12 Mb/s. We expect BlueCoDE to be a breakthrough solution for coexistence in dense Wi-Fi and BT environments.

This paper deals with the problem of severe wireless performance degradation when multiple wireless technologies are concurrently utilized in a same user device. This type of usage is already frequent in most smartphones and laptops, such as streaming Bluetooth audio while using a Wi-Fi download, and is more intensifying with IoT device deployment which triggers the coexistence of heterogeneous wireless technologies. To lower the form factor and the cost, chip vendors package multiple wireless interfaces into a single combo-chip where a common antenna is shared by multiple network technologies in a time division multiplexing manner. We issue that the careless operations of combo-chip design incur indeed performance degradation for in-device wireless coexistence and show the experimental results via TCP performance measurements in several smartphones and laptops. Our analysis reveals that the behavior negatively affects not only on the transmit power management of wireless access point, but also on the congestion control of TCP sender. We propose a cooperative switching scheme which incorporates TCP control behaviors for better coexistence and implement it on Android and Linux devices. Under the simultaneous use of in-device network interfaces, our approach led a WLAN throughput increment up to eight times without the mentioned issues. Further, this does not require any modification of TCP sender and wireless access point. Thus, the approach is directly applicable to existing mobile devices and also easily extendable to the combination of other in-device wireless technologies.

The concurrent deployment of heterogeneous wireless networks such as Wi-Fi, Bluetooth, and ZigBee has led to the severe interference problems in the 2.4GHz ISM band. In particular, ZigBee networks are susceptible to the interferences from other wireless technologies; For example, strong Wi-Fi signals trigger false alarms to ZigBee device that is performing low power idle listening and cause appreciable energy waste. In this paper, we propose a novel ZigBee signal detection scheme, called CoSense that accurately identifies ZigBee signals in the presence of the cross-technology interferences. CoSense, which is a highly reliable signal correlation technique, enjoys the following three advantages: First, CoSense reduces false wake-ups, which typically consume energy unnecessarily. Secondly, CoSense is robust against heterogeneous interference scenarios because its signal correlation feature has been shown to work well in bad channel conditions. Third, CoSense is backward-compatible and does not require to change the traditional ZigBee networks. We have implemented CoSense on the USRP/GNURadio platform in order to prove its feasibility. The results show that, under typical setting, CoSense indeed reduces the false alarm rate and its overhead is tolerable. We can conclude that CoSense saves energy by up to 63% in heterogeneous network environments where weak ZigBee signals are overwhelmed by strong signals such as Wi-Fi.

Recently, we have received a growing number of reports that complain about poor and unstable internet connections at bus stops in metro Seoul. Careful analyses led us to conclude that Wi-Fi APs equipped on buses instigate the trouble. According to the ambitious free Wi-Fi expansion plan by the city of Seoul, public buses started to equip Wi-Fi APs. As buses with APs stop and go, they actualize intermittent connection opportunities to riders waiting at the bus stops. However, the connection durations are too short such that bus APs are a nuisance rather than a convenience. We collected the basic statistics such as AP inter-arrival and sojourn times and measured link level performance metrics. We observed the effect of frequent frame losses on the TCP congestion control and eventually on the TCP throughput. We also measured the performance of applications such as PLT (Page Load Time). The measurement results showed that passing APs are useful only for some applications in very limited situations while they are virtually useless and just irritations in many cases. We also discovered that poor Wi-Fi connections pervert MPTCP; MPTCP performs worse than the generic single path TCP over the LTE network. We expect that our results will be used as the reference data in redesigning Wi-Fi offloading mechanisms as well as in planning and deploying urban Wi-Fi networks.