With the increasing reliance on technology, it has become crucial to secure every aspect of online information where pseudo random binary sequences (PRBS) can play an important role in today’s world of Internet. PRBS work in the fundamental mathematics behind the security of different protocols and cryptographic applications. This paper proposes a new PRBS namely MK (Mamun, Kumu) sequence for security applications. Proposed sequence is generated by primitive polynomial, cyclic difference set in elements of the field and binarized by quadratic residue (QR) and quadratic nonresidue (QNR). Introduction of cyclic difference set makes a special contribution to randomness of proposed sequence while QR/QNR-based binarization ensures uniformity of zeros and ones in sequence. Besides, proposed sequence has maximum cycle length and high linear complexity which are required properties for sequences to be used in security applications. Several experiments are conducted to verify randomness and results are presented in support of robustness of the proposed MK sequence. The randomness of proposed sequence is evaluated by popular statistical test suite, i.e., NIST STS 800-22 package. The test results confirmed that the proposed sequence is not affected by approximations of any kind and successfully passed all statistical tests defined in NIST STS 800-22 suite. Finally, the efficiency of proposed MK sequence is verified by comparing with some popular sequences in terms of uniformity in bit pattern distribution and linear complexity for sequences of different length. The experimental results validate that the proposed sequence has superior cryptographic properties than existing ones.

Currently, the IEEE 802.11n wireless local-area network (WLAN) has become most popular for Internet services due to simple installations, low cost devices, and easy extensions. Since access-points (APs) are often densely installed in the field, and may cause high radio interferences. To address the problem, we have studied the AP transmission power optimization method that allocates either the maximum or minimum power to each AP, such that the average signal-to-interference ratio (SIR) becomes largest. The receiving signal strength (RSS) is measured with the possible power combinations to estimate the SIR. However, this study was limited to use of the channel bonding (CB). In this paper, we investigate the feasibility of the AP transmission power optimization method under co-use of CB and non-CB in WLAN through experiments using three Raspberry Pi APs and Linux PC hosts. The experiment results confirmed that the proposed method can optimize the transmission power, once the best CB/non-CB assignment is found.

Nowadays, the IEEE 802.11n wireless local-area network (WLAN) has been extensively deployed around the world due to affordability, accessibility and expandability. As a result, a lot of access-points (APs) are closely allocated, which may cause radio interferences and degrade network performances. To solve this problem, we have studied the transmission power optimization method for concurrently communicating APs in WLAN. Either the maximum or minimum power is selected for each AP such that it gives the largest signal-to-noise ratio (SNR) of the receiving signal strength (RSS) through exhaustive measurements for the possible combinations. However, this method limits only two APs. In this paper, we present a generalization of this method for handling any number of APs in dense WLAN. To reduce SNR measurements, it sequentially selects the AP to the minimum power in descending order of the initial RSS where every AP has the maximum power. For evaluations, we conduct extensive experiments under various network topologies using Raspberry Pi for up to four APs. The results consistently show that the proposal can select the best transmission power combination for the APs, except for the cases where multiple APs are allocated in a small room. The improvements will be in future studies.

The IEEE 802.11 wireless local-area network (WLAN) has been deployed around the globe as a major Internet access medium due to its low cost and high flexibility and capacity. Unfortunately, dense wireless networks can suffer from poor performance due to high levels of radio interference resulting from adjoining access points (APs). To address this problem, we studied the AP transmission power optimization method, which selects the maximum or minimum power supplied to each AP so that the average signal-to-interference ratio (SIR) among the concurrently communicating APs is maximized.However, this method requires measurements of receiving signal strength (RSS) under all the possible combinations of powers. It may need intolerable loads and time as the number of APs increases. It also only considers the use of channel bonding (CB), although non-CB sometimes achieves higher performance under high levels of interference. In this paper, we present an AP interface setup optimization method using the throughput estimation model for concurrently communicating APs. The proposed method selects CB or non-CB in addition to the maximum or minimum power for each AP. This model approach avoids expensive costs of RSS measurements under a number of combinations. To estimate the RSS at an AP from another AP or a host, the model needs the distance and the obstacles between them, such as walls. Then, by calculating the estimated RSS with the model and calculating the SIR from them, the AP interface setups for a lot of APs in a large-scale wireless network can be optimized on a computer in a very short time. For evaluation, we conducted extensive experiments using Raspberry Pi for APs and Linux PCs for hosts under 12 network topologies in three buildings at Okayama University, Japan, and Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. The results confirm that the proposed method selects the best AP interface setup with the highest total throughput in any topology.

The accurate positioning of location in indoor environment has become crucial in many location-based services, mainly where global positioning systems (GPSs) are unavailable or fail to navigate correctly. Conventional fingerprint-based approaches face challenges with instability, low accuracy, and being sensitive to changes in the environment. This study proposes a robust fingerprint-based machine learning (ML) model for dynamic environment indoor navigation in real time. The proposed model uses link quality indicator (LQI) values from IEEE 802.15.4 as fingerprints and supervised learning algorithms, showing high accuracy and a strong ability to adapt to changes in the environment. A room within a building floor has been regarded as the unit of location identification instead of the user’s exact coordinates to make the suggested model more relevant under practical conditions. The model was trained and tested using a real LQI dataset collected from varied indoor conditions to ensure the system can adapt effectively and operate consistently in dynamic environments and signal conditions. The results show that the proposed model surpasses fingerprinting indoor navigation in room detection accuracy and flexibility to environmental changes. An implemented prototype proved the real-time capability of the proposal in smart buildings, hospitals, and industrial IoT settings.