Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies, limiting their use for lightweight, secure image encryption in resource-constrained Wireless Sensor Networks (WSNs). We propose the SPCM, a novel one-dimensional discontinuous chaotic system integrating polynomial and sine functions, leveraging a piecewise function to achieve a broad chaotic range ( $r\in [0,12]$) and a high Lyapunov exponent (5.04). Validated through nine benchmarks, including standard randomness tests, Diehard tests, and Shannon entropy (3.883), SPCM demonstrates superior randomness and high sensitivity to initial conditions. Applied to image encryption, SPCM achieves 0.152582 s (39% faster than some techniques) and 433.42 KB/s throughput (134% higher than some techniques), setting new benchmarks for chaotic map-based methods in WSNs. Chaos-based permutation and exclusive or (XOR) diffusion yield near-zero correlation in encrypted images, ensuring strong resistance to Statistical Attacks (SA) and accurate recovery. SPCM also exhibits a strong avalanche effect ( $>50\mathrm{\%}$ bit difference), making it an efficient, secure solution for WSNs in domains like healthcare and smart cities.

Content delivery networks (CDNs) lead to fast content distribution through content caching at specific CDN servers near end users. However, existing CDNs based on infrastructure cannot be employed in special cases, such as military operations. Thus, a temporary CDN without an existing infrastructure is required. To achieve this goal, we introduce a new CDN for drone-aided ad hoc networks, whereby multiple drones form ad hoc networks and quickly store specific content according to new caching algorithms. Unlike the typical CDN server, the content-caching algorithm in the proposed architecture considers the limited storage capacity of the drone. We present three content distribution algorithms that consider the constraints and mobility of drones. The main contribution of content caching for drone-aided ad hoc networks is to keep partial segments rather than whole content as well as move the drone near to area with a high volume of requests. The proposed scheme is evaluated to demonstrate its feasibility in terms of content acquisition time and utilization in several practical scenarios through simulations. Consequently, acquisition time in CDN to support drone movement is improved by approximately 50 $\mathrm{\%}$ and $40\mathrm{\%}$ rather than one in the proposed naive greedy approach as a function of content request interval and size, respectively.

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications, such as remote health monitoring, industrial monitoring, transportation, and smart agriculture. Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes. This paper presents a traffic-aware, cluster-based, and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks. The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies. The protocol considers multi-criteria attributes, i.e., energy, traffic load, and distance parameters to select the next hop for data delivery towards the base station. The performance of the proposed protocol is evaluated through the network simulator NS3.40. For different traffic rates, number of nodes, and different packet sizes, the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio, energy consumption, end-to-end delay, and network lifetime. For 100 nodes, the proposed protocol achieved a 13% improvement in packet delivery ratio, 10 ms improvement in delay, and 10 mJ improvement in average energy consumption over LoRaWAN.